Intelligent co-detection of cyclists and motorcyclists based on an improved deep learning method

Abstract

Vulnerable road user (VRU) collision avoidance is a challenge task in developing an advanced driving assistant system (ADAS). This is because motorcyclists are the most VRUs, while most existing ADAS systems are not capable of distinguishing motorcyclists from cyclists. In order to address this problem, this study proposes a new detection method based on a center and scale prediction-proposal region (CSP-PR) improved deep learning model. This new method attempts to identify the individual difference and similar appearance between cyclists and motorcyclists from different perspectives; by doing so, the cyclists and motorcyclists can be co-detected from each other to enable effective VRU collision avoidance. For this purpose, a shared salient region extraction method is first introduced based on the CSP-PR; then multiple-instance target candidate regions are generated based on the redundancy strategy; last, a multi-task learning method is proposed to train an ResNet50-deep neural network to reduce the risk of overfitting and improve the co-detection rate of the cyclists and motorcyclists. In order to verify the effectiveness of the proposed CSP-PR-ResNet50 method, a local motorcyclist database and the Tsinghua-Daimler cyclist database were combined to form an experimental dataset. The experimental results demonstrate that the proposed method is able to effectively identify the cyclists and the motorcyclists. Compared with popular algorithms, including the faster region-based convolutional neural networks, YOLOv3 (You Only Look Once v3) and single shot multibox detector, the average precision (AP) of the cyclist detection produced by the proposed method is improved by 3.69%, 17.74% and 31.22%, respectively; and the AP of the motorcyclists detection is improved by 4.29%, 15.5% and 32.57%, respectively.