Abstract: Aiming at the problem that the recognition technology of harvesting robots in crop picking was limited by complex background interference in unstructured environments, especially due to occlusion by foliage and the overlapping of fruits, resulting in lower accuracy in identification, an improved YOLOv5 algorithm was proposed based on the improved research approach involving postprocessing of the model. Initially, the centroid distance of fruit targets, the actual difference in predicted box width and height, and the intersectionoverunion of areas were collectively considered as loss terms. This was aimed at enhancing the accuracy of predicted box sizes. Furthermore, the centroid distance was utilized as a penalty term weighted by the intersectionoverunion score to improve the recognition capability for densely clustered targets. Subsequently, auxiliary training heads were incorporated to provide additional gradient information, thereby preventing overfitting. Through comparative analysis of loss values using multiple loss functions and assessing the model improve mentaccuracy, the effectiveness of the enhancements was experimentally validated. Finally, the deployment onto the robot confirmed the feasibility of the proposed improvements. The results indicated that the improved algorithm model achieved an average accuracy of 95.6%, with a recall rate of 90.1%. Compared to the preimprovement overall class accuracy, there was an increase of 0.4 percentage points in both accuracy and recall rate, meeting the recognition requirements for harvesting robots.

Key words: unstructured, tomato, target recognition, loss function optimization, improved YOLOv5

摘要： 针对采摘机器人收获技术中的识别技术受限于非结构化环境中复杂背景干扰的问题，采用改进模型后处理的研究路线，提出一种改进YOLOv5算法。首先将果实目标的中心点距离、预测框宽高实际差值与面积交并比三者共同考虑为损失项，提升预测框实际尺寸精度，再利用中心点距离作为惩罚项加权面积交并比得分，提升密集目标的识别能力，最后通过设置辅助训练头，提供更多的梯度信息以防止过拟合现象。通过多种损失函数损失值对比与模型改进精度对比试验证明改进有效性，部署至机器人验证可行性。结果表明，改进后的算法模型识别平均精度95.6%，召回率达到90.1%，相较于改进前全类精度提升0.4个百分点，召回率提升0.4个百分点，满足采摘机器人识别需求。

关键词: 非结构化, 番茄果实, 目标识别, 损失函数优化, YOLOv5算法