基于多梯度流残差结构的多类道路目标检测

IF 2.3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE journal of radio frequency identification Pub Date : 2024-02-21 DOI:10.1109/JRFID.2024.3368226
Leilei Xie;Zheng Li;Fenghua Zhu
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引用次数: 0

摘要

针对自动驾驶车辆在复杂道路场景目标检测任务中,由于目标尺度变化多样且密集、存在遮挡、光线不足等原因导致的误检和漏检问题,提出了基于YOLOv8复杂道路目标检测的改进模型YOLOv8-AUT。首先,基于注意力机制的多梯度流残差结构设计了MFR模块,并在该模块中加入平行梯度流分支,丰富了模型的梯度流,从而增强了对细节信息的提取能力,改善了对路面小目标的遗漏和误检。其次,利用全维动态卷积改进空间金字塔网络结构,增加模型的感知场,提高模型对复杂背景下不同尺度目标的检测能力。最后,引入软 NMS 抑制算法,解决了障碍物-目标密集区域目标漏检严重的问题。实验数据表明,在 BDD100K 数据集上,改进算法的平均准确率比原始算法提高了 7.7%,mAP@0.5:0.9,提高了 5.7%,证明 YOLOv8-AUT 能够更好地满足自动驾驶复杂道路场景下的目标检测需求。
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Multi-Class Road Target Detection Based on Multi-Gradient Flow Residual Structure
Aiming at the problem of false and missed detection due to the varied and dense target scale changes, the presence of occlusion, and insufficient light in the target detection task of complex road scenes for self-driving vehicles, an improved model YOLOv8-AUT based on YOLOv8 complex road target detection is proposed. Firstly, the MFR module is designed based on the multi-gradient flow residual structure of the attention mechanism, and the parallel gradient flow branches are added to the module to enrich the gradient flow of the model, so as to enhance the ability to extract the detailed information, and to improve the omission and misdetection of the small targets on the road. Secondly, the spatial pyramid network structure is improved using full-dimensional dynamic convolution to increase the sensory field of the model and improve the model’s ability to detect targets of different scales in complex backgrounds. Finally, the soft-NMS suppression algorithm is introduced to solve the problem of severe target leakage detection in obstacle-target dense regions. The experimental data show that on the BDD100K dataset, the improved algorithm improves the average accuracy mean by 7.7% compared with the original algorithm, mAP@0.5:0.9 by 5.7%, which proves that YOLOv8-AUT can better satisfy the demand for target detection in complex road scenarios of autonomous driving.
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