Road Scene Object Detection Based on Prior Saliency Information

In the field of automatic driving, road object detection and recognition is a key link, which is directly related to the driving safety of intelligent vehicles. In the driving scene, there are many kinds of objects with large difference size, which makes the convolutional network unable to fully extract the object location information, cause the low accuracy of road scene detection. To solve this problem, an improved Sa-YOLOV5s algorithm based on saliency information is proposed. Firstly, the improved semantic segmentation model (SaNet)is used to fully extract semantic information and obtain salient image. Then the salient image is fused with the convolutional layer features of different scales to enhance the discrimination between the background and the target. Finally, DIoU-NMS is used to fully calculate the positions of all bounding boxes to further reduce the situation of false detection and missed detection. By comparing with BshapeNet+ algorithm and DIDN algorithm, it is verified that the detection performance of this method is better than BshapeNet+ algorithm and DIDN algorithm on Cityscapes dataset, and the mean average precision is increased by 0.024 and 0.072 respectively. In terms of real-time detection, the detection speed reaches 33 frames per second, which meets the standard of real-time detection of 24 frames per second. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.