Real-time object detection based on YOLO-v2 for tiny vehicle object

Abstract In Automatic Driving System (ADS) and Driver Assistance System (DAS), object detection plays a vital part. Nevertheless, existing real-time detection models for tiny vehicle objects have the problems of low precision and poor performance. To solve these issues, we propose a novel real-time object detection model based on You Only Look Once Version 2 (YOLO-v2) deep learning framework for tiny vehicle objects, called Optimized You Only Look Once Version 2 (O-YOLO-v2). In the proposed model, a new structure is introduced to strengthen the feature extraction ability of the network by adding convolution layers at different locations. Meanwhile, the problem of gradient disappearance or dispersion caused by increasing network depth is solved by adding residual modules. Furthermore, in order to promote the detection accuracy of tiny vehicle objects, we combine the low-level features and high-level features of the network. The experimental findings and analysis on a KITTI dataset show that the model not only promotes the accuracy of tiny vehicle object detection but also improves the accuracy of vehicle detection (the accuracy reaches 94%) without decreasing the detection speed.