YOLO-SAT提升沙漠垃圾检测精度与效率，适配无人机实时部署。
YOLO-SAT: A Data-based and Model-based Enhanced YOLOv12 Model for Desert Waste Detection and Classification
- 基于剪枝轻量化的YOLOv12，融合自对抗训练与定制数据增强。
- 在DroneTrashNet上实现高精度（mAP提升显著）与低延迟、小模型体积。
- 专为资源受限的沙漠环境设计，适合无人机等边缘设备应用。
全球固废危机日益严峻，未来固体废物产量将大幅增长。传统垃圾收集方式在偏远或恶劣环境（如沙漠）中劳动强度大、效率低且危险。尽管计算机视觉与深度学习推动了自动化垃圾检测系统的发展，但多数研究集中于城市环境和可回收物，忽视有机与有害垃圾，且对沙漠等未充分探索地形关注不足。本文提出YOLO-SAT，一种基于剪枝轻量化YOLOv12的增强型实时目标检测框架，结合自对抗训练（SAT）与专用数据增强策略。在DroneTrashNet数据集上，该模型显著提升精度、召回率与平均精度均值（mAP），同时保持低延迟与紧凑模型规模，适用于资源受限的空中无人机部署。与现有轻量级YOLO变体对比表明，其在准确率与效率间取得最佳平衡。结果验证了数据驱动与模型优化协同对沙漠环境中鲁棒、实时垃圾检测的有效性。
原文摘要 · Abstract (English)
The global waste crisis is escalating, with solid waste generation expected to increase tremendously in the coming years. Traditional waste collection methods, particularly in remote or harsh environments like deserts, are labor-intensive, inefficient, and often hazardous. Recent advances in computer vision and deep learning have opened the door to automated waste detection systems, yet most research focuses on urban environments and recyclable materials, overlooking organic and hazardous waste and underexplored terrains such as deserts. In this work, we propose YOLO-SAT, an enhanced real-time object detection framework based on a pruned, lightweight version of YOLOv12 integrated with Self-Adversarial Training (SAT) and specialized data augmentation strategies. Using the DroneTrashNet dataset, we demonstrate significant improvements in precision, recall, and mean average precision (mAP), while achieving low latency and compact model size suitable for deployment on resource-constrained aerial drones. Benchmarking YOLO-SAT against state-of-the-art lightweight YOLO variants further highlights its optimal balance of accuracy and efficiency. Our results validate the effectiveness of combining data-centric and model-centric enhancements for robust, real-time waste detection in desert environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。