Real time segmentation method for underground track area based on improved STDC

Abstract

Currently, most underground rail transportation scenarios in China are relatively open. There are problems of operators, scattered materials, or coal slag invading the track. It poses a threat to locomotive operation. The underground track area of coal mines often presents linear or arc-shaped irregular areas, and the track gradually converges. It is difficult to accurately obtain the track range by using object recognition boxes or detecting track lines to divide the track area. Using track area segmentation can achieve pixel level accurate track area detection. Aiming at the problems of poor edge information segmentation and low real-time performance in current underground track area segmentation methods, a real-time track area segmentation method based on improved network short-term dense concatenate (STDC) is proposed. STDC is adopted as the backbone architecture to reduce the amount of network parameters and computational complexity. A feature attention module (FAM) based on channel attention mechanism is designed to capture the dependency relationships between channels and effectively refine and combine features. The feature fusion module (FFM) is used to fuse advanced semantic features with shallow features. The channel and spatial attention are utilized to enrich the fusion feature expression, effectively obtaining features and reducing feature information loss, improving model performance. Binary cross entropy loss, dice loss, and image quality loss are used to optimize the extraction of detailed information, and to improve segmentation efficiency by eliminating redundant structures. By verifying on a self built dataset, the results show the following points. The mean intersection over union (MIoU) of the improved STDC based real-time segmentation method for track area is 95.88, which is 3% higher than STDC. The number of parameters is 6.74 MiB, which is 18.3% lower than STDC. As the number of iterations increases, the optimized loss function value continues to decrease, and the decrease in function value is more significant than that of the original model. The MIoU of the improved STDC based real-time segmentation method for track area reaches 95.88%, frames per second is 37.8 frames/s, the number of parameters is 6.74 MiB, and accuray rate is 99.46%. This method can fully recognize the track area, accurately segment the track, and provide complete and accurate edge contours.

Keywords

• underground track area
• semantic segmentation
• network with short-term dense concatenate
• feature attention
• feature fusion
• feature fusion