2024 Volume 64 Issue 6 Pages 1019-1028
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.
