An improved yolov8n based dislocation detection algorithm for shaft rigid tank channel joint

As the guiding device of the hoisting system, the shaft rigid tank channel is the basis for the safe and stable operation of the hoisting system, which is prone to lateral displacement and deformation during the use process, and will cause strong vibration of the hoisting system in the event of unevenness, which seriously affects the safety of the operation of the hoisting system. In order to timely discover the faults of vertical well rigid tank channel joints and eliminate the hidden dangers of the hoisting system operation, a vertical well rigid tank channel joint misalignment detection algorithm YOLOv8n-CFW is proposed to improve YOLOv8n. First of all, for the blurred wellbore, darkness, strong light and the existence of complex background imaging environment, the YOLOv8n is insufficient to extract the image features of the rigid tank channel of the vertical well, and it incorporates the CBAM Attention Mechanism Module, which combines the channel attention mechanism with the spatial attention mechanism to form a new convolutional block structure for better feature fusion and helps the model to focus more on the important parts of the input image, thus improving the model's recognition accuracy and generalization ability, and overcoming the problem of YOLOv8n's trunk network's lack of feature extraction ability and generalization ability for locally important information under blurred, dark, and bright light environments. feature extraction ability is insufficient and the generalization ability is poor; then, to lighten the network, Faster_Block is used instead of Bottleneck to reduce the computational complexity of the C2f module, which overcomes the shortcomings of YOLOv8n network that the amount of model parameters is too large to be deployed with difficulty; and then, to inhibit the harmful gradients generated by low-quality images, the WIoU loss function is used instead of the CIoU to improve the anchor frame prediction accuracy, and the WIoU v3 loss function, which suppresses the harmful gradients generated by low-quality images by dynamically distributing the gradient gain, overcomes the insufficient localization performance of the YOLOv8n network, so that the network locates more accurately to the edge of the rigid tank channel in the joint mismatch detection, thus reducing the error, and further improves the model recognition accuracy and generalization ability; finally, the inference process in the Finally, the region of interest is set in the inference process to further suppress the background image interference, and the non-fixed focus ranging method is used to calculate the tank channel joint offset size. The experiments show that compared with the basic YOLOv8n network, the YOLOv8n-CFW detection network improves the precision P by 1.4%, the recall R by 8.2%, the average precision mAP by 6.5% from 83.8% in YOLOv8n to 90.3%, and the model size is reduced by 1.4MB compared with other YOLO algorithms in the vertical wells with rigid tank channel joints dataset. YOLO algorithms, YOLOv8n-CFW has a significant advantage in misalignment detection of rigid tank channel joints in vertical wells.