Adaptive recognition of intelligent inspection system for cable brackets in multiple assembly scenes

A safe and reliable avionics system is an important guarantee for airplanes when accomplishing all kinds of tasks. With the ever more comprehensive and intelligentized development of avionics system, the layout of cable brackets becomes more complicated, which results in the quick and reliable assembly, and inspection of cable brackets is becoming more and more demanding. Nowadays, the detection and recognition of cable brackets in aircraft assembly scenes still rely on manual labor to compare the real assembly scene with the designed two-dimensional drawings or graphs. With the increase in number and types of cable brackets, the recognition process in multi-assembly scenes becomes intricate, time-wasting, and highly mistakable. Aiming at the cable bracket recognition problems, this paper proposes a low retrain complexity hybrid model for adaptive recognition of cable brackets. Firstly, this paper builds a weight sharing feature extraction model using improved loss function and multi-scale ensemble convolutional neural network according to the analysis of cable brackets. Secondly, the support vector machine is used to classify the feature vector extracted by pre-trained feature extraction model. By this way, the hybrid model could be retrained quickly for new types of cable brackets never seen in multiple assembly scenes, under condition that bracket number and types are variable. At last, the performances of other feature extraction methods such as SIFT, HOG, and multi-layer perceptron (MLP) are evaluated for contrast with the proposed method. As shown in the results, our method obtains higher recognition accuracy.