Guo Yubin, Wu Zhipeng, You Baihao, Chen Lanqi, Zhao Jiangsan, Li Ximing
College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China.
Department of Agricultural Technology, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, NO-1431 Ås, Norway.
Animals (Basel). 2025 Apr 23;15(9):1205. doi: 10.3390/ani15091205.
Single-class object detection, which focuses on identifying, counting, and tracking a specific animal species, plays a vital role in optimizing farm operations. However, dense occlusion among individuals in group activity scenarios remains a major challenge. To address this, we propose YOLO-SDD, a dense detection network designed for single-class densely populated scenarios. First, we introduce a Wavelet-Enhanced Convolution (WEConv) to improve feature extraction under dense occlusion. Following this, we propose an occlusion perception attention mechanism (OPAM), which further enhances the model's ability to recognize occluded targets by simultaneously leveraging low-level detailed features and high-level semantic features, helping the model better handle occlusion scenarios. Lastly, a Lightweight Shared Head (LS Head) is incorporated and specifically optimized for single-class dense detection tasks, enhancing efficiency while maintaining high detection accuracy. Experimental results on the ChickenFlow dataset, which we developed specifically for broiler detection, show that the n, s, and m variants of YOLO-SDD achieve AP improvements of 2.18%, 2.13%, and 1.62% over YOLOv8n, YOLOv8s, and YOLOv8m, respectively. In addition, our model surpasses the detection performance of the latest real-time detector, YOLOv11. YOLO-SDD also achieves state-of-the-art performance on the publicly available GooseDetect and SheepCounter datasets, confirming its superior detection capability in crowded livestock settings. YOLO-SDD's high efficiency enables automated livestock tracking and counting in dense conditions, providing a robust solution for precision livestock farming.
单类目标检测专注于识别、计数和跟踪特定动物物种，在优化农场运营中起着至关重要的作用。然而，群体活动场景中个体之间的密集遮挡仍然是一个重大挑战。为了解决这个问题，我们提出了YOLO-SDD，一种专为单类密集场景设计的密集检测网络。首先，我们引入了小波增强卷积（WEConv）来改善密集遮挡下的特征提取。在此基础上，我们提出了一种遮挡感知注意力机制（OPAM），通过同时利用低级详细特征和高级语义特征，进一步增强模型识别被遮挡目标的能力，帮助模型更好地处理遮挡场景。最后，引入了轻量级共享头（LS Head）并针对单类密集检测任务进行了专门优化，在保持高检测精度的同时提高了效率。在我们专门为肉鸡检测开发的ChickenFlow数据集上的实验结果表明，YOLO-SDD的n、s和m变体分别比YOLOv8n、YOLOv8s和YOLOv8m的AP提高了2.18%、2.13%和1.62%。此外，我们的模型超越了最新实时检测器YOLOv11的检测性能。YOLO-SDD在公开可用的GooseDetect和SheepCounter数据集上也取得了领先的性能，证实了其在拥挤牲畜环境中的卓越检测能力。YOLO-SDD的高效性使得在密集条件下能够自动进行牲畜跟踪和计数，为精准畜牧业提供了一个强大的解决方案。
