An Anchor-Free Pipeline MFL Image Detection Method

To apply deep learning algorithms to magnetic flux leakage (MFL) detection, we propose an anchor-free pipeline MFL image detection method (AFMFLDM) that can simultaneously combine low latency and high accuracy. The algorithm is modified based on CenterNet. The anchor-free target detection algorithm does not need to design the anchor box size compared with the one-stage and two-stage target detection algorithms, and there is no nonmaximum suppression (NMS) process, which reduces the computational effort. Then, the backbone of this algorithm is selected as a modified PP-LCNet, which replaces the normal convolution with a depthwise separable convolution. It is supplemented with a technique of adjusting parameters to form a network similar to MobileNetV1, which ensures low computational effort and high accuracy compared with the popular feature extraction networks. Finally, a feature fusion module based on receptive field convolution (FFRF) is introduced to improve the detection accuracy. The experimental results show that the accuracy of the algorithm is 95.6% when the intersection over union (IOU) is greater than 0.5, and the inference time is 8.7 ms, which can meet the actual demand of pipeline MFL detection.