Abstract
The conveyor belt is an indispensable piece of conveying equipment for a mine whose deviation caused by roller sticky material and uneven load distribution is the most common failure during operation. In this paper, a real-time conveyor belt detection algorithm based on a multi-scale feature fusion network is proposed, which mainly includes two parts: the feature extraction module and the deviation detection module. The feature extraction module uses a multi-scale feature fusion network structure to fuse low-level features with rich position and detail information and high-level features with stronger semantic information to improve network detection performance. Depthwise separable convolutions are used to achieve real-time detection. The deviation detection module identifies and monitors the deviation fault by calculating the offset of conveyor belt. In particular, a new weighted loss function is designed to optimize the network and to improve the detection effect of the conveyor belt edge. In order to evaluate the effectiveness of the proposed method, the Canny algorithm, FCNs, UNet and Deeplab v3 networks are selected for comparison. The experimental results show that the proposed algorithm achieves 78.92% in terms of pixel accuracy (PA), and reaches 13.4 FPS (Frames per Second) with the error of less than 3.2 mm, which outperforms the other four algorithms.
Highlights
Mining operations require conveyor belts to move mined material, such as coal, from the working face over a long distance to the processing plant [1]
The algorithm, which is based on a multi-scale feature fusion network proposed in this paper, has the following characteristics: (1) Multi-scale fusion extracts global and local edge features effectively in a network structure; (2) The traditional convolutions are replaced with depthwise separable convolutions for network compression, which improves detection speed and performance to meet the requirements of various production scenarios; (3) A new weighted loss function is presented to optimize the network, which has a greater improvement in accuracy; (4) The experimental results show that the proposed method can achieve 78.92% in terms of pixel accuracy and the processing speed is 13.4 FPS with the error of less than 3.2 mm
Considering the inference time, the proposed method significantly outperforms the others by reducing the parameters of the depthwise separable convolution significantly, while the testing speed of FCN-8s, UNet and Deeplab v3 are too slow to meet the requirements of real-time detection in an industrial field
Summary
Mining operations require conveyor belts to move mined material, such as coal, from the working face over a long distance to the processing plant [1]. It has long been recognized that a belt condition monitoring system for early detection of unusual belt deviation is desirable. It is essential to study the mechanism of conveyor belt deviation [3], respond to the intelligent mine by focusing on monitoring and controlling [4] and combine it with the new generation of information technology such as cloud computing, big data and artificial intelligence to propose a more intelligent method for conveyor belt deviation detection. An increasing number of researchers have been paying attention to the detection of conveyor belt deviation, and it is a significant problem in coal mining [5,6,7,8]. Mechanical detection is used to drive the linkage mechanism through the contact between the roller and belt, Algorithms 2019, 12, 205; doi:10.3390/a12100205 www.mdpi.com/journal/algorithms
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