Fan Hongwei, Liu Jinpeng, Yan Xinshan, Zhang Chao, Cao Xiangang, Mao Qinghua
School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi'an University of Science and Technology, Xi'an 710054, China.
Sensors (Basel). 2024 Aug 14;24(16):5251. doi: 10.3390/s24165251.
Foreign objects in coal flow easily cause damage to conveyor belts, and most foreign objects are often occluded, making them difficult to detect. Aiming at solving the problems of low accuracy and efficiency in the detection of occluded targets in a low-illumination and dust fog environment, an image detection method for foreign objects is proposed. Firstly, YOLOv5s back-end processing is optimized by soft non-maximum suppression to reduce the influence of dense objects. Secondly, SimOTA label allocation is used to reduce the influence of ambiguous samples under dense occlusion. Then, Slide Loss is used to excavate difficult samples, and Inner-SIoU is used to optimize the bounding box regression loss. Finally, Group-Taylor pruning is used to compress the model. The experimental results show that the proposed method has only 4.20 × 10 parameters, a computational amount of 1.00 × 10, a model size of 1.20 MB, and an mAP of up to 91.30% on the self-built dataset. The detection speed on the different computing devices is as high as 66.31, 41.90, and 33.03 FPS. This proves that the proposed method achieves fast and high-accuracy detection of multi-layer occluded coal flow foreign objects.
煤流中的异物容易对输送带造成损坏，且大多数异物常被遮挡，难以检测。针对低光照和粉尘雾环境下遮挡目标检测准确率低、效率低的问题，提出一种异物图像检测方法。首先，通过软非极大值抑制对YOLOv5s后端处理进行优化，以减少密集物体的影响。其次，使用SimOTA标签分配来减少密集遮挡下模糊样本的影响。然后，使用滑动损失挖掘困难样本，使用内部交并比优化边界框回归损失。最后，使用分组泰勒剪枝压缩模型。实验结果表明，该方法在自建数据集上参数仅为4.20×10，计算量为1.00×10，模型大小为1.20MB，mAP高达91.30%。在不同计算设备上的检测速度高达66.31、41.90和33.03 FPS。这证明该方法实现了对多层遮挡煤流异物的快速、高精度检测。
