A method of cross-layer fusion multi-object detection and recognition based on improved faster R-CNN model in complex traffic environment ?

Improving the detection accuracy and speed is the prerequisite of multi-object recognition in the complex traffic environment. Despite object detection has made significant advances based on deep neural networks, it remains a challenge to focus on small and occlusion objects. We address this challenge by allowing multiscale fusion. We introduce a cross-layer fusion multi-object detection and recognition algorithm based on Faster R-CNN, an approach that the five-layer structure of VGG16 (Visual Geometry Group) is used to obtain more characteristic information. We implement this idea with lateral embedding the 1 & times;1 convolution kernel, max pooling and deconvolution, in conjunction with weighted balanced multi-class cross entropy loss function and Soft-NMS to control the imbalance between difficult and easy samples. Considering the actual situation in a complex traffic environment, we manually label mixed dataset. On Cityscapes and KITTI datasets, experimental results show that the proposed model achieves better effects than the current mainstream object detection models. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study proposes a multi-object detection and recognition algorithm based on Faster R-CNN, which tackles the challenge of object detection in complex traffic environments through multiscale fusion and feature enhancement. Experimental results show that the algorithm performs better on the Cityscapes and KITTI datasets compared to current mainstream models.