ANPR object detection featured image
Object detection is a task in computer vision, which requires the algorithm to predict a bounding box with a class label for each Region of Interest (ROI) in an image. Anchor-based detector have ruled this space for a long time. Recently, anchor-free detectors have started to overtake anchor-based detectors due to their lower computational complexity and efficient detections.

ANPR using NanoDet, an Anchor-free Object Detection model 

1.0 Introduction 

Object detection is a task in computer vision, which requires the algorithm to predict a bounding box with a class label for each Region of Interest (ROI) in an image. Anchor-based detectors have ruled this space for a long time. Recently, anchor-free detectors have started to overtake anchor-based detectors due to their lower computational complexity and efficient detections. NanoDet has generated interest in the AI community as it is a fast and lightweight anchor-free object detection model.  

This blog aims to introduce NanoDet – a new model which comes with less memory footprint along with better detection performance compared to anchor-based models. 

2.0 Anchor based models vs Anchor–free models 

2.1 Anchor based models 

In an anchor-based model, predefined anchor boxes of various sizes are used to calculate the location of the object in an image. In anchor-based detectors, the bounding box locations are predicted using an additional offsets regression, which can be thought of as pre-defined sliding windows or proposals that are categorized as positive or negative classes. 

2.2 Anchor–free models 

To identify and locate the objects in an image, anchor-free models use a per-pixel classification, which is comparable to segmentation. As a result, computations are simplified and the necessity for anchor boxes as a hyperparameter is eliminated. 

Conventionally, anchor-based models such as single-shot detector SSD[1], You Only Look Once YOLO[2] and region-based convolutional neural network R-CNN[3] were popular for object detection. Although anchor-based models have performed adequately well with respect to detections in real-world applications such as person detection, crowd counting, licence plate detection and recognition, there were several drawbacks.  

Some of the drawbacks of anchor-based detectors are [4]: 

  • The prediction is dependent on size, number and aspect ratios of anchor boxes. With predefined anchor boxes, detectors encounter difficulties to deal with objects of large shape variations, particularly for small objects  
  • Large number of anchor boxes are required to predict the objects. Most of these anchor boxes return negative values resulting in an imbalance between positive & negative samples 
  • Complex computation to calculate the Intersection Over Union (IOU) with ground truth to classify each anchor box as positive or negative 

Anchor-Free Object Detectors address the above drawbacks. In an anchor-free model, every pixel in the feature map is predicted within an object box, similar to segmentation.  

A few prominent advantages of anchor-free object detections are [5]: 

  • Detection framework works like classical fully convolutional segmentation networks 
  • There is no dependence on the size of anchor boxes for detection 
  • Detection becomes anchor free which lowers the computational complexity and design hyper parameters. 

Since anchor-free models tend to be faster with better results and can be deployed on low-cost edge devices with lesser memory footprint, we chose NanoDet, an anchor-free model for detecting numbers from a number plate. Before looking into an example let’s briefly review NanoDet. 

3.0 NanoDet 

NanoDet [6]. is a lightweight, superfast, real-time object detection model that comes with the following features. 

  • Model file size 980KB(INT8) or 1.8MB(FP16). 
  • Speed: 97fps (10.23ms) on mobile ARM CPU. 
  • High accuracy up to 34.3 mAP@0.5:0.95 on COCO-dataset and still real-time on CPU. 
  • Easier to train and has much lower GPU memory cost than other models. The model can be trained with a batch-size of 80 on GTX1060-6GB 

3.1 Architecture of NanoDet 

  • Backbone: NanoDet uses ShufflenetV2 [7] which is a very robust & cost-effective structure for mobile devices as a backbone. 
  • Neck: NanoDet uses Pyramid Attention Network (PAN) for extracting feature maps but all the convolutions except for 1×1 convolutions were removed for a lighter weight model. 
  • Head: NanoDet uses depth wise convolution at the head to make it light weight. The border regression & classification is calculated using the same convolutions and later split into two parts to reduce calculations. 

4.0 Automatic Number Plate Recognition (ANPR) 

The objective of this experiment is to detect only the large font numbers in a number plate in real-time, which can be even used for digit recognition, given the training data has seen such labeled data during training.  

4.1 Dataset 

A dataset with more than 2000 original number plate images were collected from various sources which consist of license plates pertaining to different colour backgrounds from various vehicles. To augment original data, synthetic data has been used to enhance the count of training image samples to attain the expected result. 

The dataset was labeled using Labeling and corresponding .XML of the image annotation file was created. The list of classes includes digits from 0 to 9 as the class labels. 

4.2 Experiments 

The dataset was divided into training and validation with the ratio 80% – 20% respectively. The model was trained for 1000 epochs using Pytorch and later converted to tflite. The size of the model was 1.9 MB in float16 that got reduced to 970 KB with int8 conversion. This memory footprint helps to get deployed on a low-cost edge device such as R-pi where memory is a concern. 

Several models were trained and tested using the same dataset before NanoDet was used. On the Ignitarium custom dataset, models like YOLOv4-Tiny had a mAP of 20.8 with a size of 3.2MB, and YOLOX-Nano, an anchor-free model, had a mAP of 33.6. As the preliminary results were satisfactory with a mAP of 42, to further reduce the size, layer customization was done on the model. The customized NanoDet model was retrained for 2000 epochs with the same amount of data. The results of customized NanoDet model (38.9 mAP) were comparable to the original model. The customized model after converting to int8 had a size of ~753 KB without much drop in performance. 

Model  mAP  Size 
NanoDet   42.0  1.9MB (float16) 
YOLOX-Nano  33.6  3.2MB (float16) 
YOLOv4-Tiny  20.8  12 MB (float16) 
NanoDet (custom)  38.9  753KB (int8) 

4.3 Results 

The results obtained from NanoDet were comparable with the results from other detectors like YOLOX-Nano, YOLOv4-Tiny. The custom NanoDet model had a mAP of 38.9 with a size of 753KB. The test results were compared on the same bench marking data created in-house to use across all the models for testing. NanoDet outperformed in terms of detection accuracy and memory footprint on the Ignitarium custom test data. 

5.0 Conclusion 

An anchor-free & proposal-free single-stage detector called NanoDet is used to detect numbers in a number plate. This model is lightweight with good performance. It is comparable with popular anchor-based models like YOLO & SSD. The model completely avoids all computation and hyper-parameters related to anchor boxes and solves the object detection in a per-pixel prediction fashion. NanoDet can be used for various other detection applications in the future as well, which comes as a strong alternative to anchored models and has diverse applications. 

For an alternative AI-based approach towards ANPR, please refer Ignitarium’s blog accessible here.


  1. “SSD: Single Shot MultiBox, Detector”, Wei Liu, Dragomir Anguelov, et al.2016. 

  1. “You Only Look Once:Unified, Real-Time Object Detection”, Joseph Redmon∗, Santosh Divvala∗†, Ross Girshick, Ali Farhadi University of Washington∗, Allen Institute for AI†, Facebook AI Research. 

  1. “Rich feature hierarchies for accurate object detection and semantic segmentation Tech report (v5)” Ross Girshick Jeff Donahue Trevor Darrell Jitendra Malik 

  1. Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection, Shifeng Zhang, et al.2022  

  1. FCOS: Fully Convolutional One-Stage Object Detection, Zhi Tian, et al.2019 


  1. “ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design” Ningning Ma1,2 Xiangyu Zhang ?1 Hai-Tao Zheng2 Jian Sun1 1 Megvii Inc (Face++) 2 Tsinghua University, 2018 


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Human Pose Detection & Classification

Some Buildings in a city


  • Suitable for real time detection on edge devices
  • Detects human pose / key points and recognizes movement / behavior
  • Light weight deep learning models with good accuracy and performance

Target Markets:

  • Patient Monitoring in Hospitals
  • Surveillance
  • Sports/Exercise Pose Estimation
  • Retail Analytics

OCR / Pattern Recognition

Some Buildings in a city

Use cases :

  • Analog dial reading
  • Digital meter reading
  • Label recognition
  • Document OCR

Highlights :

  • Configurable for text or pattern recognition
  • Simultaneous Analog and Digital Dial reading
  • Lightweight implementation

Behavior Monitoring

Some Buildings in a city

Use cases :

  • Fall Detection
  • Social Distancing

Highlights :

  • Can define region of interest to monitor
  • Multi-subject monitoring
  • Multi-camera monitoring
  • Alarm triggers

Attire & PPE Detection

Some Buildings in a city

Use cases :

  • PPE Checks
  • Disallowed attire checks

Use cases :

  • Non-intrusive adherence checks
  • Customizable attire checks
  • Post-deployment trainable


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    Real Time Color Detection​

    Use cases :

    • Machine vision applications such as color sorter or food defect detection

    Highlights :

    • Color detection algorithm with real time performance
    • Detects as close to human vison as possible including color shade discrimination
    • GPGPU based algorithm on NVIDIA CUDA and Snapdragon Adreno GPU
    • Extremely low latency (a few 10s of milliseconds) for detection
    • Portable onto different hardware platforms

    Missing Artifact Detection

    Use cases :

    • Detection of missing components during various stages of manufacturing of industrial parts
    • Examples include : missing nuts and bolts, missing ridges, missing grooves on plastic and metal blocks

    Highlights :

    • Custom neural network and algorithms to achieve high accuracy and inference speed
    • Single-pass detection of many categories of missing artifacts
    • In-field trainable neural networks with dynamic addition of new artifact categories
    • Implementation using low cost cameras and not expensive machine-vision cameras
    • Learning via the use of minimal training sets
    • Options to implement the neural network on GPU or CPU based systems

    Real Time Manufacturing Line Inspection

    Use cases :

    • Detection of defects on the surface of manufactured goods (metal, plastic, glass, food, etc.)
    • Can be integrated into the overall automated QA infrastructure on an assembly line.

    Highlights :

    • Custom neural network and algorithms to achieve high accuracy and inference speed
    • Use of consumer or industrial grade cameras
    • Requires only a few hundred images during the training phase
    • Supports incremental training of the neural network with data augmentation
    • Allows implementation on low cost GPU or CPU based platforms

    Ground Based Infrastructure analytics

    Some Buildings in a city

    Use cases :

    • Rail tracks (public transport, mining, etc.)
    • Highways
    • Tunnels

    Highlights :

    • Analysis of video and images from 2D & 3D RGB camera sensors
    • Multi sensor support (X-ray, thermal, radar, etc.)
    • Detection of anomalies in peripheral areas of core infrastructure (Ex: vegetation or stones near rail tracks)

    Aerial Analytics

    Use cases :

    • Rail track defect detection
    • Tower defect detection: Structural analysis of Power
      transmission towers
    • infrastructure mapping

    Highlights :

    • Defect detection from a distance
    • Non-intrusive
    • Automatic video capture with perfectly centered ROI
    • No manual intervention is required by a pilot for
      camera positioning


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    Founder and Managing director of Ignitarium, Sanjay has been responsible for defining Ignitarium’s core values, which encompass the organisation’s approach towards clients, partners, and all internal stakeholders, and in establishing an innovation and value-driven organisational culture.


    Prior to founding Ignitarium in 2012, Sanjay spent the initial 22 years of his career with the VLSI and Systems Business unit at Wipro Technologies. In his formative years, Sanjay worked in diverse engineering roles in Electronic hardware design, ASIC design, and custom library development. Sanjay later handled a flagship – multi-million dollar, 600-engineer strong – Semiconductor & Embedded account owning complete Delivery and Business responsibility.


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      Board Member

      Ramesh was the Founder and CEO of Insta Health Solutions, a software products company focused on providing complete hospital and clinic management solutions for hospitals and clinics in India, the Middle East, Southeast Asia, and Africa. He raised Series A funds from Inventus Capital and then subsequently sold the company to Practo Technologies, India. Post-sale, he held the role of SVP and Head of the Insta BU for 4 years. He has now retired from full-time employment and is working as a consultant and board member.


      Prior to Insta, Ramesh had a 25-year-long career at Wipro Technologies where he was the President of the $1B Telecom and Product Engineering Solutions business heading a team of 19,000 people with a truly global operations footprint. Among his other key roles at Wipro, he was a member of Wipro's Corporate Executive Council and was Chief Technology Officer.


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      Pradeep graduated in Industrial Engineering and Management, went on to secure an MBA from CUSAT, and cleared UGN Net in Management. He also had teaching stints at his alma mater, CUSAT, and other management institutes like DCSMAT. A certified P3O (Portfolio, Program & Project Management) from the Office of Government Commerce, UK, Pradeep has been recognized for key contributions in the Management domain, at his previous organizations, Wipro & Virtusa.

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      Azif handled key accounts and sales process initiatives at Sankalp Semiconductors. Azif has pursued entrepreneurial interests in the past and was associated with multiple start-ups in various executive roles. His start-up was successful in raising seed funds from Nokia, India. During his tenure at Nokia, he played a key role in driving product evangelism and customer success functions for the multimedia division.


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      Vice President – Automotive Technology


      Sujeet is responsible for driving innovation in Automotive software, identifying Automotive technology trends and advancements, evaluating their potential impact, and development of solutions to meet the needs of our Automotive customers.

      At Ignitarium, he was previously responsible for the growth and P&L of the Embedded Business unit focusing on Multimedia, Automotive, and Platform software.

      Prior to joining Ignitarium in 2016, Sujeet has had a career spanning more than 16 years at Wipro. During this stint, he has played diverse roles from Solution Architect to Presales Lead covering various domains. His technical expertise lies in the areas of Telecom, Embedded Systems, Wireless, Networking, SoC modeling, and Automotive. He has been honored as a Distinguished Member of the Technical Staff at Wipro and has multiple patents granted in the areas of Networking and IoT Security.

      Sujeet holds a degree in Computer Science from Government Engineering College, Thrissur.


      RAJIN RAVIMONY Distinguished Engineer


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      At Ignitarium, Rajin plays the role of Distinguished Engineer for complex SoCs and systems. He's an expert in ARM-based designs having architected more than a dozen SoCs and played hands-on design roles in several tens more. His core areas of specialization include security and functional safety architecture (IEC61508 and ISO26262) of automotive systems, RTL implementation of math intensive signal processing blocks as well as design of video processing and related multimedia blocks.


      Prior to Ignitarium, Rajin worked at Wipro Technologies for 14 years where he held roles of architect and consultant for several VLSI designs in the automotive and consumer domains.


      Rajin holds an MS in Micro-electronics from BITS Pilani.


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      Executive Vice President, Strategy


      As EVP, of Strategy at Ignitarium, Siby anchors multiple functions spanning investor community relations, business growth, technology initiatives as well and operational excellence.


      Siby has over 31 years of experience in the semiconductor industry. In his last role at Wipro Technologies, he headed the Semiconductor Industry Practice Group where he was responsible for business growth and engineering delivery for all of Wipro’s semiconductor customers. Prior to that, he held a vast array of crucial roles at Wipro including Chief Technologist & Vice President, CTO Office, Global Delivery Head for Product Engineering Services, Business Head of Semiconductor & Consumer Electronics, and Head of Unified Competency Framework. He was instrumental in growing Wipro’s semiconductor business to over $100 million within 5 years and turning around its Consumer Electronics business in less than 2 years. In addition, he was the Engineering Manager for Enthink Inc., a semiconductor IP-focused subsidiary of Wipro. Prior to that, Siby was the Technical Lead for several of the most prestigious system engineering projects executed by Wipro R&D.


      Siby has held a host of deeply impactful positions, which included representing Wipro in various World Economic Forum working groups on Industrial IOT and as a member of IEEE’s IOT Steering Committee.


      He completed his MTech. in Electrical Engineering (Information and Control) from IIT, Kanpur and his BTech. from NIT, Calicut


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      Chief Technology Officer


      As CTO, Sujeeth is responsible for defining the technology roadmap, driving IP & solution development, and transitioning these technology components into practically deployable product engineering use cases.


      With a career spanning over 30+ years, Sujeeth Joseph is a semiconductor industry veteran in the SoC, System and Product architecture space. At SanDisk India, he was Director of Architecture for the USD $2B Removable Products Group. Simultaneously, he also headed the SanDisk India Patenting function, the Retail Competitive Analysis Group and drove academic research programs with premier Indian academic Institutes. Prior to SanDisk, he was Chief Architect of the Semiconductor & Systems BU (SnS) of Wipro Technologies. Over a 19-year career at Wipro, he has played hands-on and leadership roles across all phases of the ASIC and System design flow.


      He graduated in Electronics Engineering from Bombay University in 1991.


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      As Ignitarium's Co-founder and COO, Sujith is responsible for driving the operational efficiency and streamlining process across the organization. He is also responsible for the growth and P&L of the Semiconductor Business Unit.


      Apart from establishing a compelling story in VLSI, Sujith was responsible for Ignitarium's foray into nascent technology areas like AI, ML, Computer Vision, and IoT, nurturing them in our R&D Lab - "The Crucible".


      Prior to founding Ignitarium, Sujith played the role of a VLSI architect at Wipro Technologies for 13 years. In true hands-on mode, he has built ASICs and FPGAs for the Multimedia, Telecommunication, and Healthcare domains and has provided technical leadership for many flagship projects executed by Wipro.


      Sujith graduated from NIT - Calicut in the year 2000 in Electronics and Communications Engineering and thereafter he has successfully completed a one-year executive program in Business Management from IIM Calcutta.


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      Co-founder & CRO

      As Co-founder and Chief Revenue Officer of Ignitarium, Ramesh has been responsible for global business and marketing as well as building trusted customer relationships upholding the company's core values.

      Ramesh has over 25 years of experience in the Semiconductor Industry covering all aspects of IC design. Prior to Ignitarium, Ramesh was a key member of the senior management team of the semiconductor division at Wipro Technologies. Ramesh has played key roles in Semiconductor Delivery and Pre-sales at a global level.

      Ramesh graduated in Electronics Engineering from Model Engineering College, Cochin, and has a Postgraduate degree in Microelectronics from BITS Pilani.