# FPGA-Based Linear Detection Algorithm of an Underground Inspection Robot
## 2 Previous Work  
In 2008, [PERSON] et al. designed a real-time detection system for hot spots in substations [9]. The system consists of a patrol robot equipped with Wi-Fi and infrared cameras and an upper computer in the control room. The robot moves through the steel cable in the substation, and the infrared camera is positioned to different equipment detection points through the pan-tilt system. The robot communicates with the upper computer located in the control room. The upper computer controls the movement of the robot and analyzes the images collected by the robot. After that, this computer detects hot spots in the substation. In 2008, [PERSON] et al. [8] proposed a line segment detector (LSD). The LSD algorithm merges the pixels with the same edge direction through the method of region growth to realize the detection of the line segment area. Without adjusting the parameters, good detection results could be obtained. In 2013, [PERSON], [PERSON], and others studied a surveillance and patrol robot based on SIP protocol for home security [10]. The robot is equipped with multiple sensors and a high-definition camera. The robot is activated at a preset time, regularly inspects the home space, and senses and tracks moving objects during the inspection process. Through the SIP protocol, it initiates a communication request to the mobile device that belongs to the head of the household, then establishes an audio and video stream between the robot and the mobile device. In February 2020, the National Development and Reform Commission (China), the National Energy Administration, and another eight ministries and commissions jointly issued the \"Guiding Opinions on Accelerating the Development of Intelligent Coal Mines\", marking that the coal industry has entered a new stage of achieving intelligent mining. With the development of intelligence and digitization, the coal industry urgently needs to change its traditional high-intensity work methods and realize humanized or even unmanned mining as soon as possible [11]. In recent years, due to the various shortcomings of manual inspection in belt conveyors, the research on automatic inspection devices of belt conveyors has attracted more and more scholarly attention. In 2019, [PERSON] from Xi'an University of Science and Technology built an orbital wireless power supply inspection robot [12]. In view of the failure of the belt conveyor roller, the audio signal of the belt conveyor is dynamically collected by the robot, and then the pulse excitation frequency in the audio signal is extracted. After that, the fault samples are classified and trained through a BP neural network model. Finally, the high-precision identification of roller failures is realized. The rapid development of computers, sensors, and logic digital devices has promoted the popularization of visual inspection technology in various fields of automation. Becauseof its non-contact, intuitive, and high-precision characteristics, visual inspection technology has become one of the main research directions for fault diagnosis and status detection in the industrial field [13]. Hence, using machine vision to detect and diagnose the operating status of the belt conveyor has become an effective means. In 2019, [PERSON] of China University of Mining and Technology [14] designed an orbital inspection robot that carries multiple types of sensors and uploads the collected image data, temperature, humidity, and smoke density to the upper computer via Wi-Fi. And then, aiming at diagnosing conveyor belt deviation faults using the upper computer, she designed a method of deviation diagnosis based on deep learning. In 2019, [PERSON] [15] used Intel's FPGA as a computing chip and used a combination of coordinate statistics and grouping approximation methods to identify the lateral and torsional deviations of a conveyor belt, which achieved high detection accuracy and real-time performance.