基于改进YOLOv5的仓储物体检测研究

Research on warehouse object detection based on improved YOLOv5

针对仓储复杂多样的环境以及对仓储物体传统检测模型性能较低的问题,本文基于PaddlePaddle框架,提出一种改进YOLOv5的仓储物体检测模型YOLOv5-CE。首先,为提高在复杂多样环境下对仓储物体检测,采用ConvNeXt网络替换原YOLOv5的主干网络,以提高对中小型仓储物体的特征提取能力;其次,为提高模型的收敛速度和物体的检测精度,采用EIoU Loss代替原模型的损失函数;最后,通过利用自制仓储训练集进行多模型对比实验。实验结果表明,在对货物(cargo)、托盘(tray)、叉车(forklift)进行检测时,改进后的模型平均检测精度均值(mAP@0.5:0.95)达到89.8%,相比原YOLOv5提升1.1%,其中在小尺度仓储物体上,提升4.2%;在中、大尺度仓储物体检测上,皆提升1%;对于小仓储物体检测的平均召回率由61.1%提升至66.8%。与YOLOv6、YOLOX、YOLOv7、Faster R-CNN等其他模型相比,YOLOv5-CE均体现出更好的精度。同时,相比上述模型,YOLOv5-CE在模型参数量、检测速度以及检测精度上,也取得良好的平衡,能够较好地满足对不同大小、不同种类仓储物体的精准检测。

In order to solve the problem of complex and diverse warehouse environment and the low performance of traditional warehouse object detection models, this paper proposes an improved YOLOv5 (You Only Look Once version 5) warehouse object detection model YOLOv5-CE (YOLOv5-ConvNeXt EIoU) which based on the PaddlePaddle framework. Firstly, to improve the detection of warehouse objects in complex and diverse environments, the ConvNeXt network is used to replace the original YOLOv5 backbone network to improve the feature extraction ability of small and medium-sized warehouse objects. Secondly, in order to improve the convergence speed of the model and the detection accuracy of objects, EIoU Loss (efficient intersection over union loss) is used to replace the loss function of the original model. Finally, by using the self-made warehousing training set to carry out multi-model comparison experiments. The experimental results show that when detecting cargo, tray and forklift, the average detection accuracy of the improved model (mAP@0.5:0.95, mean average precision@0.5:0.95) reaches 89.8%, which is 1.1 percentage points higher than the original YOLOv5, of which 4.2 percentage points is increased in small-scale warehousing objects;in the detection of medium and large-scale warehouse objects, it increased by 1 percentage point. The average recall rate for small warehouse objects increased from 61.1% to 66.8%. Compared with other models such as YOLOv6, YOLOX, YOLOv7, and Faster R-CNN, YOLOv5-CE all shows better accuracy. At the same time, in view of the above model, YOLOv5-CE also achieves a good balance in the number of model parameters, detection speed and detection accuracy, which can better meet the precise detection of warehouse objects of different sizes and types.