Lightweight environment sensing algorithm for intelligent driving based on improved YOLOv7

Abstract

AbstractAccurately and quickly detecting obstacles ahead is a prerequisite for intelligent driving. The combined detection scheme of light detection and ranging (LiDAR) and the camera is far more capable of coping with complex road conditions than a single sensor. However, immediately afterward, ensuring the real‐time performance of the sensing algorithms through a significantly increased amount of computation has become a new challenge. For this purpose, the paper introduces an improved dynamic obstacle detection algorithm based on YOLOv7 (You Only Look Once version 7) to overcome the drawbacks of slow and unstable detection of traditional methods. Concretely, Mobilenetv3 supplants the backbone network utilized in the original YOLOv7 architecture, thereby achieving a reduction in computational overhead. It integrates a specialized layer for the detection of small‐scale targets and incorporates a convolutional block attention module to enhance detection efficacy for diminutive obstacles. Furthermore, the framework adopts the Efficient Intersection over Union Loss function, which is specifically designed to mitigate the issue of mutual occlusion among detected objects. On a dataset consisting of 27,362 labelled KITTI data samples, the improved YOLOv7 algorithm achieves 92.6% mean average precision and 82 frames per second, which reduces the Model_size by 85.9% and loses only 1.5% accuracy compared with the traditional YOLOv7 algorithm. In addition, this paper builds a virtual scene to test the improved algorithm and fuses LiDAR and camera data. Experimental results conducted on a test vehicle equipped with a camera and LiDAR sensor demonstrate the effectiveness and significant performance of the method. The improved obstacle detection algorithm proposed in this research can significantly reduce the computational cost of the environment perception task, meet the requirements of real‐world applications, and is crucial for achieving safer and smarter driving.