Abstract: Aiming at the problems that the traditional contact deviation detection method in coal mines cannot detect the deviation amount, and the two-dimensional image detection method is affected by the harsh underground environment, a quantitative detection method for belt conveyor deviation in coal mines based on three-dimensional point cloud is proposed. To address the low efficiency of deviation detection caused by the large amount of point cloud data, the point cloud data of the belt conveyor is collected through the ROI (Region of Interest) local area sampling method, and the voxel centroid downsampling of the point cloud data is carried out to streamline the point cloud data. In order to filter out the noise points and redundant information points such as idler rollers and the frame in the point cloud data, an improved point cloud segmentation method combining Fast Euclidean Clustering (FEC) and region growing is proposed to achieve the filtering of redundant point cloud information such as idler rollers and the fast and accurate segmentation of the point cloud data on the conveyor belt surface. To solve the problems of low accuracy and efficiency in extracting the edge point information of the 3D point cloud of the conveyor belt, an adaptive RANSAC (Random Sample Consensus) inner point extraction method is proposed to introduce SPRT (Sequential Probability Ratio Test) and AIC (Akaike Information Criterion) to obtain the optimal model parameters of the conveyor belt edge line, realizing the efficient and accurate fitting of the conveyor belt edge line. Aiming at the problem of precise quantitative detection of conveyor belt deflection, a quantitative deflection detection method based on the spatial position deviation between the center line of the conveyor belt and the center line of the frame is proposed to accurately detect the deviation direction and amount of the conveyor belt. The experimental platform for belt conveyor deviation detection is built, and experiments are designed in two different experimental environments of normal lighting and simulated fog and dust to verify the quantitative deviation detection method for belt conveyors. Experimental results demonstrate that the proposed method achieves high-precision and real-time detection of conveyor belt deviation under various conditions. Under normal lighting, the average measurement error is 0.05 cm with a detection speed of 0.551 seconds per frame in the unloaded state, while under loaded conditions, the error increases to 0.12 cm and the detection speed decreases to 0.729 seconds per frame. The method maintains an error below 0.103 cm at low belt speeds and within 0.12 cm at high speeds, fulfilling practical accuracy requirements. Even in simulated fog and dust environments with severe point cloud loss, the system achieves an average error of 0.11 cm and a detection speed of 0.565 seconds per frame. Overall, the maximum average error across all test scenarios is 0.12 cm, and the fastest detection speed reaches 0.551 seconds per frame, both of which satisfy the real-time and accuracy demands of conveyor belt deviation monitoring in coal mine production.