RDDPA: Real-time Defect Detection via Pruning Algorithm on Steel Surface
Real-time object detectors deployed on general-purpose graphics processing units (GPUs) or embedded devices allow their mass usage in industrial applications at an affordable cost. However, existing state-of-the-art object detectors are difficult to meet the requirements of high accuracy and low inf...
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Veröffentlicht in: | ISIJ International 2024/04/15, Vol.64(6), pp.1019-1028 |
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Sprache: | eng |
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Zusammenfassung: | Real-time object detectors deployed on general-purpose graphics processing units (GPUs) or embedded devices allow their mass usage in industrial applications at an affordable cost. However, existing state-of-the-art object detectors are difficult to meet the requirements of high accuracy and low inference latency simultaneously in industrial applications on general-purpose devices. In this work, we propose RDDPA, a fast and accurate defect detection framework. RDDPA adopts a novel end-to-end pruning scheme, which can prune the detection network from scratch and achieve real-time detection on general-purpose devices. Additionally, we have developed a new training scheme to minimize the accuracy loss associated with the pruning process. Experimental results on a standard steel surface defect dataset indicate that our model achieves 79.2% mAP (mean Average Precision) at 103.7 FPS (Frames Per Second) on a single mid-end Titan X GPU as well as 40.1 FPS on a single low-end GTX 960M GPU, and outperforms the state-of-the-art defect detectors by about 20× speedup with considerable or higher accuracy. |
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ISSN: | 0915-1559 1347-5460 |
DOI: | 10.2355/isijinternational.ISIJINT-2023-360 |