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

翻译：本文针对计算机视觉中的小目标、密集目标及重叠目标检测这一重大挑战展开研究。我们重点综述了基于深度学习的监督式方法，并通过新构建的包含逾1万张图像和12万个实例的数据集，系统比较了各方法在工业回收流程应用场景中的性能表现、检测精度和计算效率。通过对比分析，我们识别出当前最可靠的检测系统及其针对性解决的特定挑战。此外，我们深入探讨了数据增强与合成图像技术的优势。基于分析结果，我们提出可能提升小目标/密集目标/重叠目标检测系统效能的未来研究方向与创新解决方案。研究范围涵盖工业回收流程中的目标检测、长度测量及异常检测。该异常检测策略对图像分辨率和缩放比例的变化具有鲁棒性，可确保工业应用中的可靠性能。所提出的数据集、方法及评估代码存储库见：https://github.com/o-messai/SDOOD