针对工业回收中的小目标检测难题，构建超10k图像新数据集并评估YOLO性能。
Small Object Detection in Industrial Recycling: A New Dataset and YOLO Performance Evaluation
- 构建含120k实例的工业回收小目标数据集，支持密集重叠场景评测。
- 在10,000+图像上验证多种YOLO模型，揭示各系统准确率与效率差异。
- 适合工业视觉、小目标检测研究者，尤其关注数据增强与真实场景部署。
本文聚焦于计算机视觉中难以处理的小目标、密集且重叠物体的检测问题。研究基于深度学习的监督方法，提出一个包含超过10,000张图像和120,000个实例的新数据集，并在此基础上对现有系统进行详细对比分析，涵盖其在工业回收场景下的性能、精度与计算效率。通过比较，识别出当前最可靠的检测系统及其应对的具体挑战。同时探索数据增强与合成图像带来的收益。基于分析结果，提出未来改进方向与创新解决方案。研究范围包括物体检测、长度测量及异常检测，其中异常检测策略对图像分辨率与缩放级别变化具有鲁棒性，确保工业应用中稳定表现。相关数据集、方法与评估代码已开源：https://github.com/o-messai/SDOOD。
原文摘要 · Abstract (English)
In this paper, we address the problem of detecting small, dense, and overlapping objects, a major challenge in computer vision. Our focus is on reviewing proposed methods based on deep learning supervised approaches. We provide a detailed comparison of these systems on a new dataset of more than 10k images and 120k instances, highlighting their performance, accuracy, and computational efficiency in the industrial recycling process use case. Through this comparative analysis, we identify the most reliable systems currently available and the specific challenges they are designed to tackle. Furthermore, we explore the benefits of data augmentation and synthetic images. Based on our analysis, we also propose potential future directions and innovative solutions that could enhance the effectiveness of small, dense and overlapped object detection systems. The scope of our investigations encompasses object detection, length measurement, and anomaly detection within the context of the recycling process. The anomaly detection strategy is robust against variations in image resolution and zoom levels, ensuring reliable performance in industrial applications. The repository of the proposed dataset, methods and evaluation codes can be found at: https://github.com/o-messai/SDOOD
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