文献类型：期刊文献

英文题名：EcoDetect-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Domestic Waste Exposure in Intricate Environmental Landscapes

作者：Liu, Shenlin[1];Chen, Ruihan[1,2];Ye, Minhua[3];Luo, Jiawei[1];Yang, Derong[1];Dai, Ming[1]

机构：[1]Guangdong Ocean Univ, Sch Math & Comp, Zhanjiang 524008, Peoples R China;[2]Int Macau Inst Acad Res, Artificial Intelligence Res Inst, Macau 999078, Peoples R China;[3]Guangdong Ocean Univ, Coll Ocean Engn & Energy, Zhanjiang 524088, Peoples R China

年份：2024

卷号：24

期号：14

外文期刊名：SENSORS

收录：SCI-EXPANDED(收录号：WOS:001277535600001)、、EI(收录号：20243116781946)、Scopus(收录号：2-s2.0-85199752920)、WOS

基金：This research was supported by a special grant from the Guangdong Basic and Applied Basic Research Foundation under grant no. 2023A1515011326, the Program for Scientific Research Start-up Funds of Guangdong Ocean University under grant no. 060302102101, the Guangdong Provincial Science and Technology Innovation Strategy under grant no. pdjh2023b0247,the National College Students Innovation and Entrepreneurship Training Program under grant no. 202410566027, and the Guangdong Ocean University Undergraduate Innovation Team Project under grant no. CXTD2023014

语种：英文

外文关键词：domestic waste; intricate environmental landscapes; lightweight; P2; CBAM; BiFPN

外文摘要：In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model's efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model's capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO's adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development.