基于深度学习的轻量化红外弱小车辆目标检测算法研究

伴随高速飞行器的不断发展，目标检测识别作为精确制导的关键一环，需要更高实时性、高准确性地进行目标定位和识别。当前，针对装甲车辆、车辆阵地等时间敏感目标精确检测识别的需求日益迫切，深度学习算法在特征提取及分类器设计上具备优势。文中以特定复杂背景下的小尺寸红外车辆目标为研究对象，针对样本数据少、平台资源受限、实时性要求高、检测精度高等需求，开展基于红外弱小车辆目标检测识别的轻量化深度学习算法研究。项目基于YOLOv5算法进行轻量化剪裁，减小模型的结构，提高实时性；提出了混合域注意力机制模块EPA，该模块通过不降维的局部跨信道交互策略使算法更快速有效地关注重要通道，抑制无效通道，并将通道注意力机制与空间注意力机制结合，使得算法更关注与目标相关的像素信息。提出了残差密集注意模块（RDAB），该模块由密集残差块与注意力机制EPA构成，通过密集卷积层来提取充分的局部特征，通过注意力机制获取更有效的通道与像素信息，可以使得算法以较小的模型结构获得较好的检测效果。运用设计的网络对数据增广后的小尺寸红外车辆目标数据进行检测识别，并与多种典型算法进行对比实验。由实验结果可知，文中提出的JH-YOLOv5-RDAB网络检测识别效果优于其他网络，权重大小仅为6.6 MB，仅为YOLOv5s算法模型权重的一半，但算法检测效果更优，与93.7 MB的YOLOv5l算法的检测效果接近，mAP50达到95.1%。实验结果表明:该网络在红外弱小车辆目标检测上的优越性和可行性。

Lightweight infrared dim vehicle target detection algorithm based on deep learning

With the continuous development of high-speed aircraft, target detection and recognition, as a key part of precision guidance, requires higher real-time and high-accuracy target positioning and recognition. At present, the need for accurate detection and identification of time-sensitive targets such as armored vehicles and vehicle positions is increasingly urgent. Deep learning algorithms have advantages in feature extraction and classifier design. This paper takes the small-sized infrared vehicle target under a specific complex background as the research object, and develops a lightweight deep learning algorithm based on infrared dim vehicle target detection and recognition to meet the needs of less sample data, limited platform resources, high real-time requirements, and high detection accuracy. The project is light-weight cut based on the YOLOv5 algorithm, reduce the structure of the model and improve the real-time performance; a hybrid domain attention mechanism module EPA is proposed, which enables the algorithm to focus on important channels more quickly and effectively through a local cross-channel interaction strategy without dimensionality reduction. Suppressing invalid channels and combining the channel attention mechanism with the spatial attention mechanism makes the algorithm pay more attention to the pixel information related to the target. The Residual Dense Attention Module (RDAB) is proposed, which is composed of dense residual blocks and attention mechanism EPA. It extracts sufficient local features through dense convolutional layers, and obtains more effective channel and pixel information through attention mechanism, which can make the algorithm obtain better detection effect. Detect and identify the small-size infrared vehicle target data after data augmentation, and compare experiments with a variety of typical algorithms. It can be seen from the experimental results that the detection and recognition effect of the JH-YOLOv5-RDAB network proposed in this paper is better than other networks, and the weight size is only 6.6 MB, which is only half of the weight of the YOLOv5s algorithm model, but the algorithm detection effect is better, and the detection effect of the algorithm is close YOLOv5l whose weight size is 93.7 MB, with mAP50 reaching 95.1%. The experimental results show the superiority and feasibility of this network in infrared dim vehicle target detection.