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.

部署在通用图形处理单元 (GPU) 或嵌入式设备上的实时物体检测器允许以可承受的成本在工业应用中大量使用。然而，现有最先进的物体检测器很难同时满足通用设备上工业应用中高精度和低推理延迟的要求。在这项工作中，我们提出了 RDDPA，一种快速准确的缺陷检测框架。 RDDPA采用新颖的端到端剪枝方案，可以从头开始剪枝检测网络，实现在通用设备上的实时检测。此外，我们还开发了一种新的训练方案，以最大限度地减少与修剪过程相关的准确性损失。标准钢表面缺陷数据集上的实验结果表明，我们的模型在单个中端 Titan X GPU 上以 103.7 FPS（每秒帧数）实现了 79.2% mAP（平均平均精度），在单个低端 GPU 上实现了 40.1 FPS GTX 960M GPU，比最先进的缺陷检测器性能提高约 20 倍，并且具有相当大或更高的精度。