Ren Yuanming, Li Yizhe, Gao Xinya
College of Science, Qingdao University of Technology, 777 Jialingjiang East Road, Huangdao District, Qingdao 266520, China.
Sensors (Basel). 2024 Jul 4;24(13):4339. doi: 10.3390/s24134339.
With the advancement in living standards, there has been a significant surge in the quantity and diversity of household waste. To safeguard the environment and optimize resource utilization, there is an urgent demand for effective and cost-efficient intelligent waste classification methodologies. This study presents MRS-YOLO (Multi-Resolution Strategy-YOLO), a waste detection and classification model. The paper introduces the SlideLoss_IOU technique for detecting small objects, integrates RepViT of the Transformer mechanism, and devises a novel feature extraction strategy by amalgamating multi-dimensional and dynamic convolution mechanisms. These enhancements not only elevate the detection accuracy and speed but also bolster the robustness of the current YOLO model. Validation conducted on a dataset comprising 12,072 samples across 10 categories, including recyclable metal and paper, reveals a 3.6% enhancement in mAP50% accuracy compared to YOLOv8, coupled with a 15.09% reduction in volume. Furthermore, the model demonstrates improved accuracy in detecting small targets and exhibits comprehensive detection capabilities across diverse scenarios. For transparency and to facilitate further research, the source code and related datasets used in this study have been made publicly available at GitHub.
随着生活水平的提高，家庭垃圾的数量和种类显著增加。为了保护环境并优化资源利用，迫切需要有效且经济高效的智能垃圾分类方法。本研究提出了MRS-YOLO（多分辨率策略-You Only Look Once），一种垃圾检测和分类模型。本文介绍了用于检测小物体的SlideLoss_IOU技术，集成了Transformer机制的RepViT，并通过融合多维和动态卷积机制设计了一种新颖的特征提取策略。这些改进不仅提高了检测精度和速度，还增强了当前YOLO模型的鲁棒性。在包含10个类别的12072个样本的数据集上进行验证，包括可回收金属和纸张，结果显示与YOLOv8相比，mAP50%精度提高了3.6%，体积减少了15.09%。此外，该模型在检测小目标方面表现出更高的精度，并在各种场景中展现出全面的检测能力。为了保证透明度并便于进一步研究，本研究中使用的源代码和相关数据集已在GitHub上公开提供。
