Abstract:To address the issues of existing conveyor belt foreign object detection models in coal mines, which perform poorly in low-light environments, miss elongated and small foreign objects, and have a large model size that hinders deployment on edge devices, this paper proposes a low-light coal mine conveyor belt foreign object detection algorithm based on an improved YOLOv8. First, image enhancement techniques are applied to preprocess low-light images to enhance the effective feature information of foreign objects on the coal mine conveyor belt. Next, dynamic snake convolution is introduced into the model′s backbone network to dynamically adjust the convolution kernel shape, improving the model′s focus on elongated foreign objects. Additionally, a slim-neck design paradigm is used to modify the neck network, significantly reducing the model′s parameters while maintaining learning capability. Finally, the Inner-CIoU loss function is employed to replace the CIoU loss function, accelerating the model′s convergence and improving its detection performance for elongated and small foreign objects. Experimental results show that, compared to the baseline model, the improved algorithm increases the average detection accuracy by 1.6%, reduces the model size by 29.7%, and improves the detection speed (FPS) by 59%, validating its effectiveness. In comparison with other advanced models, it is proved that the proposed algorithm still has strong recognition ability in complex environment.

Abstract:The detection method of longitudinal tear of single signal conveyor belt based on image or sound is often affected by light and noise. In order to overcome this limitation, a damage detection method based on multi scale feature cross fusion is proposed. Firstly, Log-Mel feature extraction algorithm is used to upgrade one-dimensional sound signal to two-dimensional spectrum; Secondly, a dual input neural network is built to extract the features of the image and the sound spectrogram at the same time, and they are cross fused at different scales; Finally, according to the fused features, the damage classification is determined by the loss function. Through experiments, the detection accuracy, sensitivity of scratch and longitudinal tear of this method can reach 97.37%, 96.53% and 98.67% respectively, which is 7.04%, 6.96% and 6.4% higher than the existing audio-visual decision level fusion method. Therefore, this method can better meet the reliability requirements of conveyor belt damage detection.

Abstract:Unavoidably, mining tunnels have locomotives, long conveyor belts, cages, metal mesh supporting and so on these objects, The existence of these objects affects electromagnetic wave propagation in tunnels to some extent. The text regards the tunnel as a waveguide, through experimental measurement and fitting method to study on attenuation of electromagnetic wave in conveyor belt roadway. The experimental results show that: the attenuation of the electromagnetic wave is connected with the existence of long conveyor belts and the received frequency; The existence of conveyor belts increases the attenuation of the electromagnetic wave; the higher the frequency of the electromagnetic wave is, the smaller the attenuation is; 900 MHz is the optimum frequency to study mine wireless communication; In the roadway with width greater than height, horizontal mode is more suitable for communication than vertical mode. The results in this paper provide more powerful support for the coal mine roadway wireless communication, and provide reference value and practical significance for the further study of the electromagnetic wave propagation characteristics.