Vehicle Make Recognition using YOLOv4 Object Detector

Introduction

A Python example for using Spectrico's car brand classifier. It consists of an object detector for finding the cars, and a classifier to recognize the brands of the detected cars. The object detector is an implementation of YOLOv4 (OpenCV DNN backend). YOLOv4 weights were downloaded from AlexeyAB/darknet. The classifier is based on MobileNet v3 (Alibaba MNN backend).

The full version recognizes the make, model, and color of the vehicles. Here is a web demo to test it: Vehicle Make and Model Recognition

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Object Detection and Classification in images

This example takes an image as input, detects the cars using YOLOv4 object detector, crops the car images, resizes them to the input size of the classifier, and recognizes the brand of each car. The result is shown on the display and saved as output.jpg image file.

Usage

Use --help to see usage of car_brand_classifier_yolo4.py:

$ python car_brand_classifier_yolo4.py --image cars.jpg

$ python car_brand_classifier_yolo4.py [-h] [--yolo MODEL_PATH] [--confidence CONFIDENCE] [--threshold THRESHOLD] [--image]

required arguments:
  -i, --image              path to input image

optional arguments:
  -h, --help               show this help message and exit
  -y, --yolo MODEL_PATH    path to YOLO model weight file, default yolo-coco
  --confidence CONFIDENCE  minimum probability to filter weak detections, default 0.5
  --threshold THRESHOLD    threshold when applying non-maxima suppression, default 0.3

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Dependencies

pip install numpy

pip install opencv-python

pip install MNN

If you use Windows, the OpenCV package is recommended to be installed from: https://www.lfd.uci.edu/~gohlke/pythonlibs/

yolov4.weights must be downloaded from https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights and saved in folder yolov4

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Credits

The examples are based on the tutorial by Adrian Rosebrock: YOLO object detection with OpenCV

The YOLOv4 object detector is from: https://github.com/AlexeyAB/darknet

@article{bochkovskiy2020yolov4,
  title={YOLOv4: Optimal Speed and Accuracy of Object Detection},
  author={Bochkovskiy, Alexey and Wang, Chien-Yao and Liao, Hong-Yuan Mark},
  journal={arXiv preprint arXiv:2004.10934},
  year={2020}
}

The car brand classifier is based on MobileNetV3 mobile architecture: Searching for MobileNetV3

@inproceedings{howard2019searching,
  title={Searching for mobilenetv3},
  author={Howard, Andrew and Sandler, Mark and Chu, Grace and Chen, Liang-Chieh and Chen, Bo and Tan, Mingxing and Wang, Weijun and Zhu, Yukun and Pang, Ruoming and Vasudevan, Vijay and others},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={1314--1324},
  year={2019}
}

The runtime library of the classifier is MNN

@inproceedings{alibaba2020mnn,
  author = {Jiang, Xiaotang and Wang, Huan and Chen, Yiliu and Wu, Ziqi and Wang, Lichuan and Zou, Bin and Yang, Yafeng and Cui, Zongyang and Cai, Yu and Yu, Tianhang and Lv, Chengfei and Wu, Zhihua},
  title = {MNN: A Universal and Efficient Inference Engine},
  booktitle = {MLSys},
  year = {2020}
}