计算机工程与应用2024，Vol.60Issue(11)：165-172,8.DOI:10.3778/j.issn.1002-8331.2306-0315
改进YOLOv7的自动驾驶目标检测算法
Improved YOLOv7 Automatic Driving Object Detection Algorithm
摘要
Abstract
It is very important for autonomous driving vehicles to accurately detect objects such as vehicles and pedestrians on the road in real time.Aiming at the problems of missed detection and poor detection effect of small targets in the auton-omous driving scene,this paper proposes an automatic driving target detection algorithm that improves the YOLOv7 algo-rithm.Firstly,it modifies the modules in the network to expand the receptive field,reduces the size of the receptive field module,and improves the speed of the model and enhances the ability to extract image feature information.Secondly,the paper introduces the BRA attention mechanism at the output of the backbone network to improve the model's ability to small target objects.Finally,it replaces the original CIOU loss function of the algorithm with the EIOU loss function to minimize the difference between the height and width of the predicted frame and the real frame,and speeds up the conver-gence of the model while achieving better positioning results.The experimental results show that:on the KITTI dataset,when the improved YOLOv7 algorithm performs target detection,its mAP reaches 94.7%,which is 3.1 percentage points higher than the original YOLOv7 algorithm,and it has achieved higher detection accuracy in small target object detec-tion.It effectively improves the model's detection effect on small targets.关键词
自动驾驶/小目标检测/YOLOv7/注意力机制/损失函数Key words
autonomous driving/small target detection/YOLOv7/attention mechanism/loss function分类
信息技术与安全科学引用本文复制引用
胡淼,姜麟,陶友凤,张志坚..改进YOLOv7的自动驾驶目标检测算法[J].计算机工程与应用,2024,60(11):165-172,8.基金项目
国家自然科学基金(11761042). （11761042）
