Autonomous unmanned aerial vehicle based intelligent inspection system for equipment, facilities, and the environment along railway lines and method thereof

An automatic intelligent inspection system for the equipment, facilities and surrounding environment along a railway line, comprising:

• an unmanned aerial vehicle (UAV) configured to fly along the railway line according to a preset trajectory, acquire and store image information of the equipment and facilities and surrounding environment along the railway line from a plurality of angles, and transmit the image information to a remote server;
• a movable ground base station configured to enable the UAV to replace batteries and load, dump data locally, and take off and land autonomously by using a ground landing platform;
• a remote server configured to receive and store the image information transmitted by the UAV;
• and
• a working computer configured to receive the image information acquired by the UAV and transmitted by the remote server, and analyze and compute the image information, so as to obtain an inspection result;
• characterized in that:
• the working computer comprises an image processing module, which detects the obtained image by using a deep learning network model trained with a database in advance, obtaining any object that is suspected to involve a defect by analysis, and sends the object for manual review while calculating for the specific geographic coordinate information of the defect, reports the defect for early warning after the defect is confirmed by the manual review, informs the operation and maintenance personnel to handle the defect, and stores and records the defect,
• wherein the image processing module comprises an image preprocessing and integration algorithm, a deep learning network model and a detection result post-processing algorithm, wherein the deep learning network model comprises a detection algorithm for fastener defect for railways that is based on CYOLO, a rail surface segmentation algorithm that is based on rail boundary guidance saliency detection network (RBGNet), a detection algorithm for bridge steel structure surface defect for bridges that is based on improved YOLOv5, and an abnormality detection algorithm for railway surrounding environment that is based on multi-source data fusion for environments.