In order to automatically realize detection of aircraft skin damage

a machine vision detection method based on the improvement of channel redundancy for YOLOv7 is proposed. Firstly

aiming at the characteristics of the single background for the aircraft skin damage dataset

an improved algorithm of enhanced neck feature fusion is proposed

which improves the recognition accuracy and detection speed of aircraft skin damage. Secondly

in order to solve the problem of convolution channel redundancy for the backbone feature extraction network

PConv (Partial convolution) is introduced

and the lightweight of the backbone feature extraction network is proposed

which reduces the number of parameters for the model and improves the efficiency of damage identification. In the experimental part

different improved algorithms of enhanced neck feature fusion were first explored on the aircraft skin damage dataset

and the optimal improvement method was determined. Then

ablation and comparative experiments were carried out on the aircraft skin damage dataset

and compared with the original YOLOv7 algorithm

the mAP (Mean average precision) is increased by 2.3%

the FPS (Frames per second) is increased by 22.1 f/s

and the number of model parameters is decreased by 34.13%. Finally

the improved YOLOv7 model is compared with the mainstream object detection model

which proves the advanced nature of the improved algorithm.