Abstract
The detection of defects in cylinder bores is crucial for the industrial manufacturing of automobile engines. Current efforts which exhibit unstable accuracies, time inefficiencies and high-cost expenditures, have been mainly initiated by well-trained inspectors. In this paper, we build on the detection framework FasterRCNN to propose an integrated defect detection method based on context encoder and perception-enhanced aggregation for cylinder bores of automobile engines, named CBDetector. The improvements are twofold. The context encoder, which is a locally sensitive and globally covered integrated encoder composed of a CNN and an improved Transformer architecture used for well-rounded feature extraction, is proposed. To robustly perceive full scale cylinder bore defects, a perception-enhanced feature path aggregation unit is introduced. Extensive experiments conducted on our established dataset HIT-EngD and a public steel dataset NEU-DET demonstrate the SOTA performance of the CBDetector, with mAP50 increases of 22.7 and 7.8 compared to FasterRCNN integrated with Feature Pyramid Network on HIT-EngD and NEU-DET. Moreover, our method can run at a high frame rate (~10 FPS, Nvidia-A100).
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