# Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network
## 1 Introduction  
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]. As the main transportation equipment in the coal industry and other industries [2], conveyor belts will deviate during operation because of uneven stress on the surface, which results in material spilling, property damage and even personnel injury. It has long been recognized that a belt condition monitoring system for early detection of unusual belt deviation is desirable. Thus, 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]. Initial detection of conveyor belt deviation mainly relies on manual inspection, which is labor intensive and prone to errors and omissions. Then, mechanical and photoelectric detection devices were introduced [9]. Mechanical detection is used to drive the linkage mechanism through the contact between the roller and belt,while photoelectric detection mainly relies on wireless sensor modules to achieve this. [PERSON] et al. used the joint sensor and the hall sensor to monitor the deviation of the conveyor belt [10]. When the joint of a conveyor belt with a magnetic disc passes through the joint sensor for length measuring, the hall sensor would sense a series of pulses. Then, when the conveyor belt deviates beyond a certain range, the hall sensor cannot detect the pulse signal in order to determine whether the belt deviates. However, the mechanical device is only suitable for conveyor belts with low rotational speed and is seldom used in practice. While photoelectric detection equipment mainly relies on a wireless sensor module, its detection accuracy depends too much on the installation density of the sensor. In a harsh and complex environment, the sensor is easily damaged, and it is difficult to ensure the reliability of the detection. With the development of computer technology, machine vision has gradually become an active research direction of conveyor belt deviation monitoring. [PERSON] et al. proposed a detection approach based on visual computing that was comprised of three steps, namely image filtering and edge enhancement, edge detection using the Canny algorithm [11] and the Hough transform [12] according to the linear characteristics of the conveyor belt edge [13]. [PERSON] et al. introduced a gray-scale average method for the fast segmentation of conveyor and background [14]. By mapping the binary image to a one-dimensional fault eige function, a deviation eigenvector consisting of a deviation angle and offset was designed for deviation detection. [PERSON] et al. presented a multi-view image adaptive enhancement method for conveyor belt fault detection and developed a mine conveyor belt online detection system to realize real-time detection of belt deviation, surface damage and longitudinal tear [15]. Traditional computer vision methods are sensitive to noise, so they can detect other edges such as a coal mine and material transported on the conveyor belt. In the place where the conveyor belt occluded, there will be discontinuity in the detection results. In addition, the parameters of the algorithm need to adjust many times for different scenarios. In recent years, deep learning has made remarkable achievements in computer vision. Deep learning is broadly used in image recognition, object detection and classification [16; 17; 18]. It has achieved state-of-the-art results in those fields. In this work, a novel algorithm based on a multi-scale feature fusion network is presented to achieve real-time conveyor belt deviation detection.