An industrial part recognition algorithm combining multiscale feature extraction and lightweight improvement

Aiming at the problems of low accuracy and large number of parameters of the current general-purpose detection methods in coping with the tasks of complex background, part occlusion and multi-scale part recognition, a lightweight industrial part detection algorithm is proposed on the basis of YOLOv8s. Designing C2f-DRRCB efficient feature extraction module in the backbone network to improve the multi-scale feature fusion performance; by introducing a lightweight Adown downsampling module, more feature-critical information is retained while reducing the number of model parameters and computation; the EMA multi-scale attention mechanism is added at the end of the backbone network to improve the network's attention to the important feature regions and reduce the background information's interference; Design of a new lightweight inspection head, SCAD-Head, to improve accurate localization of part targets by capturing long range dependencies and shared convolution. The improved algorithm is applied to the SIP-17 industrial part synthetic dataset for validation, and the results show that compared with the baseline YOLOv8s model, the average detection accuracy of the improved model is improved by 5.7%, the number of parameters is reduced by 35.85%, and the amount of computation is reduced by 27.72%, which is also a certain degree of improvement in the detection performance compared with the other latest detection algorithms.