基于红外图像的目标检测算法分析  

Analysis of Target Detection Algorithm Based on Infrared Images

作　　者：花成才 磨少清[1,2] 陈怡霖 胡海 吴思雨 HUA Chengcai;MO Shaoqing;CHEN Yilin;HU Hai;WU Siyu(School of Automotive and Transportation,Tianjin University of Technology and Education,Tianjin 300222,China;National and Local Joint Engineering Research Center of Intelligent Vehicle-Road Cooperation and Safety Technology(Tianjin),Tianjin 300222,China)

机构地区：[1]天津职业技术师范大学汽车与交通学院,天津300222 [2]智能车路协同与安全技术国家地方联合工程研究中心(天津),天津300222

出　　处：《汽车实用技术》2024年第2期59-66,共8页Automobile Applied Technology

摘　　要：针对车载红外图像细节信息表现不明显、对比度低、成像效果差等缺点导致检测效率不高的问题,文章提出了一种基于YOLOv5s改进的目标检测网络。首先在头部网络中添加一个基于注意力机制的动态探测头,其特征层间的注意力机制用于尺度感知,空间位置间的注意力机制用于空间感知,输出通道内的注意力机制用于任务感知,这使网络更加重点关注检测任务中相关联的前景目标,提升模型目标检测头的表达能力。然后在训练时用MPDIOU替换CIOU边界框损失函数,提升模型的定位精度与效率。最后把轻量级网络FasterNet添加到颈部网络末端中的C3模块,提升模型的实时性。实验结果表明,改进后的网络模型较改进前原始网络模型的mAP提升了2.1%,模型权重大小几乎不变,满足体积小与实时性的需求,适用于车载嵌入式系统中。A target detection network based on the improvement of YOLOv5s is proposed to solve the problem of low detection efficiency due to the shortcomings of vehicle-mounted infrared image,such as inconspicuous performance of detail information,low contrast,and poor imaging effect.Firstly,a dynamic detection head based on an attention mechanism is added to the head network,with an attention mechanism between feature layers for scale perception,an attention mechanism between spatial positions for spatial perception,and an attention mechanism within the output channel for task perception,which makes the network focus more on detecting the foreground target associated with the task,and improves the expressive ability of the model target detection head.Then the CIOU bounding box loss function is replaced by MPDIOU during training to improve the localization accuracy and efficiency of the model.Finally,the lightweight network FasterNet is added to the C3 module in the end of the neck network to improve the real-time performance of the model.The experimental results show that the improved network model improves the mAP by 2.1%compared with the original network model before the improvement,and the weight size of the model is almost unchanged.It meets the requirements of small size and real-time,and is suitable for in-vehicle embedded systems.

关 键 词：目标检测 YOLOv5s 注意力机制 损失函数 车载红外图像 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]