Multi-Class Vehicle Detection Using VDnet in Heterogeneous Traffic

Intelligent vehicles detection (IVD) provides information to manage traffic efficiently, drive autonomous vehicles and feed data to intelligent traffic management systems (ITMS). IVD is a challenging task for the close proximity vehicles in lane-less traffic and heterogeneous systems. Most vehicle detection models are complex and limited to multi-scale feature extraction due to the involvement of existing feature extraction backbones. Also, they do not include heterogeneous traffic vehicles, usually present in developing countries. Therefore, this paper proposes a multi-class vehicle detection (MCVD) model to detect vehicles in heterogeneous traffic using a realistic traffic dataset from a developing country. MCVD is a deep learning (DL) model that consists of a convolutional neural network backbone called VDnet, a light fusion bi-directional feature pyramid network (LFBFPN) and a modified vehicle detection head (MVDH). VDnet extracts multi-scale features from the traffic input images using feature reuse methods. LFBFPN combines these features bi-directionally and provides robust feature maps. Finally, MVDH is applied to detect multi-class vehicles and classify them into respective categories. The proposed model achieves 91.45% mean average precision (mAP) on the heterogeneous traffic labeled dataset (HTLD). The proposed MCVD is tested over Nvidia Jetson TX2 edge computing boards to verify the real-time performance. It achieves 17 frames per second (FPS) on TX2. The performance evaluation results indicate that the proposed MCVD model is fast, accurate and better than the existing works.