ROAD OBJECT DETECTION WITH DEEP LEARNING
ABSTRACT:
Road safety is a major concern today. To overcome that realistic environmental issues it is required to develop suitable tools or GUI etc. Deep Automated driving and vehicle safety systems need object detection. It is important that object detection be accurate overall and robust to weather and environmental conditions and run in real-time. As a consequence of this approach, they require image processing algorithms to inspect the contents of images. This article compares the accuracy of a few major image processing algorithms: Deep Neural Networks (DNN) Ref [2], Region-based Fully Convolutional Networks (R-FCN), Mask Region-based Convolutional Neural Networks (Mask R-CNN), Single Shot Multi-Box Detector (SSD), RetinaNet, and You Only Look Once (YOLO) Ref [1]. Here we have taken care of Helmet-No Helmet detection and license plate photo capturing.
CONCLUSION
Analysed IDD Lite dataset, and developed a program to detect Helmet and Helmet Objects on a Road Object images. Learned how to work on large-scale projects, and use pre-trained models to implement the solution. Successfully implemented YOLOV3 algorithm in DNN to detect Road Objects in Python.
FUTURE SCOPE
It can be developed further to detect more types of Road Traffic Rule Violations to improve the efficiency of Traffic Control Systems and also immediate notification to bike users for not wearing helmet.