Non-destructive testing (NDT) of magnetic materials such as aviation parts is an indispensable part of the civil aviation maintenance industry. The NDT of such metal materials often uses magnetic particle inspection (MPI) technology. This paper proposes an improved DeepLabv3+ semantic segmentation algorithm for automatic defect detection of aviation ferromagnetic parts after MPI. In the network structure, lightweight MobileNetV2 is the backbone feature extraction network. The Dense Atrous Spatial Pyramid Pooling (DenseASPP) structure is used to strengthen feature extraction. The influence of three different DenseASPP structures on the recognition effect is compared in the experiment. At the same time, the decoder is further optimized. The experimental results show that the Ours-DeepLabv3+ network model can effectively for automatic defect detection of aviation ferromagnetic parts after MPI. The Precision, Recall, F1-score, and IOU are 81.64%, 83.12%, 82.37%, and 71.23%, respectively, which are 7.48%, 5.45%, 6.50%, and 10.1% higher than the original DeepLabv3+, and defect detail segmentation is more accurate. Compared with other semantic segmentation algorithms, this method can effectively improve the accuracy of defect detection of aviation ferromagnetic parts and meet the requirements of defect detection.

The design of a whisker sensor, inspired by mammalian whisker characteristics, is presented in this paper. It uses a novel spring structure to transfer the deformation generated by the whisker tip when it touches an object at the base, which drives the permanent magnet installed at the base to change its position. It achieves precise positioning of the object by using the magnetic induction intensity data output from the Hall sensor MLX90393. Based on the results of the finite element model analysis, the detection range of the whisker sensor can be expanded by replacing the artificial whisker material and selecting a permanent magnet of a suitable size. Calibration experiments and positioning tests were conducted on the sensor. The experimental results showed that the detection radius of the sensor was 24, 30, 33, and 39 mm for the carbon fiber, acrylic, acrylonitrile butadiene styrene plastic (ABS), and nylon whiskers, respectively, when they were matched with a NdFeB annular permanent magnet with an aperture of 3 mm and a thickness of 3 mm. The sensor is small and simple to manufacture with good sensitivity, linearity, hysteresis, and repeatability. The maximum positioning errors of the X and Y positions in the detection plane of the sensor were within ±1.3 mm, and the positioning was accurate. The sensor can be used to identify the shape of an object.