目的 提高工业分拣上常见的纹理多样、随机堆放盒体的检测与识别能力。方法 提出一种基于YOLOv5的旋转目标检测算法，该算法包括目标分类、位姿角度识别和边界框位置定位3个模块功能。目标分类模块中，通过自建数据集和设计8种目标分类标签以供模型分类学习;位姿角度识别模块对YOLOv5头网络中添加角度预测分支，引入环形平滑标签角度分类方法，实现分拣盒体的旋转角度精准检测;在边界框位置定位模块上，使用LCIoU回归框损失函数来计算旋转框回归损失，得到紧密包裹目标位置的边界框。结果 改进的YOLOv5算法在自建数据集上检测精度达到95.03%，在机器人多物体分拣实验中的准确率可达100%。结论 本文算法在盒体处于散乱堆放、密集堆放、堆叠堆放场景下均具有较高的定位与识别精度。

The work aims to improve the detection and recognition ability of boxes with various textures and scattered stacking, which are common in industrial sorting. A rotating target detection algorithm based on YOLOv5 was proposed. This algorithm included three modules:target classification, pose angle recognition and boundary box location. In the target classification module, self-built data sets and eight target classification labels were designed for model classification learning; In the pose angle recognition module, an angle prediction branch was added to the YOLOv5-head network. The angle classification method of circular smooth label was introduced to realize accurate detection of rotation angle of sorting boxes; In the boundary box location module, the LCIoU regression box loss function was used to calculate the regression loss of the rotating box, and the boundary box that tightly wrapped the target position was obtained. The detection accuracy of the improved YOLOv5 algorithm in the self-built data set reached 95.03%. In the robot multi object sorting experiment, the accuracy rate reached 100%. The algorithm in this paper has high positioning and identification accuracy when boxes are in scattered, dense and stacked conditions.