PerspectiveNet: An Object Detection Method With Adaptive Perspective Box Network Based on Density-Aware
Cui‐Jin Li, Zhong Qu, Sheng-Ye Wang
Abstract
Aiming at the problems of object mutual occlusion and inaccurate confidence in complex traffic environments, we propose an adaptive network object detection algorithm with a perspective box based on density-aware. Firstly, an adaptive convolutional neural network is designed according to the complexity of image density. Res2Net-101 is used as the backbone. For the images with objects concentrated on both sides and occluded each other more, a high-density network is selected to improve the accuracy of object detection, and a low-density network is selected for the images with significant and simple objects. Secondly, in the high-density network, to improve the accuracy of object detection and reduce the missed detection problem caused by occlusion, a perspective box is added to the detection head, and the transparent box is fused with the prediction box to obtain more accurate positioning. Finally, we propose the perspective loss function, which is based on the repulsion loss of focusing smooth function and combined with feature loss and classification loss to form the overall loss of the model. The experimental results show that the model has a good detection effect compared with the state-of-the-art object detection model in complex traffic environments. Without reducing the detection speed, the mAP of PerspectiveNet on the KITTI dataset is 86.2%, which is 3.6% higher than that of VarifocalNet. On the Cityscapes dataset, the mAP is 96.3%, which is 1.7% higher than that of VarifocalNet.