基于CBAM-YOLOv5的煤矿输送带异物检测

Foreign object detection in coal mine conveyor belt based on CBAM-YOLOv5

输送带是矿井下煤炭运输的重要设备之一,运行过程中由于大块煤、矸石、锚杆、槽钢等异物混入易导致皮带撕裂故障发生,严重影响煤矿安全生产,甚至威胁矿工生命安全。为了实现煤矿井下输送带上大块异物的自动、快速以及准确检测,设计了一种基于计算机视觉技术的大块异物检测方法。针对输送带中异物目标图像受煤尘干扰、输送带高速运动以及光照不均等影响造成传统图像检测算法难以准确检测问题,提出一种融合卷积块注意力模型的YOLOv5目标检测算法,记为CBAM-YOLOv5。首先,通过自适应直方图均衡化算法来增强煤矿井下输送带图像的对比度,减少煤尘干扰;然后,针对输送带高速运动易导致待检测目标图像模糊进而造成目标难以被准确检测的问题,在YOLOv5算法框架下通过引入深度可分离卷积提高网络检测速度,并通过优化检测网络的损失函数提高整个网络的检测精度;其次,针对受光照不均影响导致异物目标难以被准确检测的问题,通过在YOLOv5检测网络中引入卷积块注意力模型来提升图像中异物目标的显著度,增强异物目标在检测网络中的特征表达能力,进而提高异物目标的检测精度;最后,利用某煤矿井下输送带监控视频数据制备训练样本和测试样本,并将提出的算法与4种经典目标检测算法进行对比。实验结果表明:所提出的检测算法可以较好的解决异物目标检测时易受煤尘干扰、输送带高速运动以及光照不均对目标检测精度的影响,对于分辨率为1 280×720的图像平均检测精度可达94.7%,检测速度为31 fps。

Coal mine conveyor belt is one of the important equipment for underground coal transportation. During the operation, due to the mixing of large coal, gangue, anchor rod, channel steel and other foreign matters, it is easy to lead to a belt tearing failure, which seriously affects the safe production of coal mine and even threatens the miners’ life. In order to realize the automatic, rapid and accurate detection of large foreign bodies on conveyor belt in coal mine, a detection method of large foreign bodies based on computer vision technology was designed. Aiming at the problem that the foreign object image in the conveyor belt is difficult to be accurately detected by the traditional image detection algorithm due to the interference of conveyor belt coal dust, high-speed movement of conveyor belt and uneven illumination, a YOLOv5 target detection algorithm based on the convolutional block attention model was proposed, denoted as CBAM-YOLOv5. First, the adaptive histogram equalization algorithm is used to enhance the contrast of the coal mine underground conveyor belt image and reduce coal dust interference. Then, for the high-speed movement of the conveyor belt, the image of the target to be detected is likely to be blurred and the target is difficult to be accurately detected. Under the framework of the YOLOv5 algorithm, the detection speed of the network is improved by introducing a deep separable convolution, and the detection accuracy of the entire network is improved by optimizing the loss function of the detection network. Secondly, in view of the problem that the foreign object is difficult to be accurately detected due to the uneven illumination, the convolutional block attention model is introduced in the YOLOv5 detection network to increase the saliency of foreign objects in the image, enhance the feature expression ability of foreign objects in the detection network, and thereby improve the detection accuracy of foreign objects. Finally, the monitoring video data of a coal mine underground conveyor belt is used to prepare training samples and test samples, and the proposed algorithm is compared with four classic target detection algorithms. The experimental results show that the proposed detection algorithm can better solve the influence of coal dust interference, high-speed movement of the conveyor belt and uneven illumination on the target detection accuracy. The average detection accuracy can reach 94.7% for the image with resolution of 1 280×720,and the detection speed is 31 fps.