基于改进RT-DETR的锻件表面缺陷检测算法OA

The forging surface defects are harmful with low detection efficiency.To address the existing problems of forg-ing surface defects detection,this paper proposes an algorithm based on improved RT-DETR.First,it collects magnetic particle inspection images as datasets in the vehicle steering knuckle manufacturing workshop of Hubei Sanhuan Forging Co.Then,a lightweight cross-stage heat conduction module is proposed,which introduces a simulated heat diffusion pro-cess into the frequency-domain modeling mechanism to achieve global perception and suppress high-frequency noise;meanwhile,it introduces context guide feature pyramid network to realize multi-scale feature fusion through dynamic channel alignment and spatial attention guidance,thereby enhancing semantic consistency and contextual integration of targets;finally,it uses dynamic position bias(DPB)module to enhance the extracting ability of cross-scale features.The experimental results on the forging surface crack dataset show that the mAP value reaches 87.9%,and the parameters and FLOPs are reduced by 20.7%and 9.3%,which is better than other mainstream algorithms.On the NEU-DET dataset,the improved RT-DETR model improves 1.2 percentage points in mAP compared to the benchmark model,which proves that the algorithm is generalizable.In conclusion,the algorithm has improved accuracy and reduced model complexity,it is suitable for deployment and application in real manufacturing situations.