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Abstract：
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.
摘要：
为了应对复杂的城市街道环境中垃圾的准确识别和定位的挑战，本文提出了EcoDetect-YOLO，基于YOLOv5s框架的垃圾暴露检测算法，利用本研究构建的复杂环境废物暴露检测数据集。最初，卷积块注意模块(CBAM)被集成在特征金字塔网络的第二级(P2)和特征金字塔网络的第三级(P3)层之间,以优化相关垃圾特征的提取,同时减轻背景噪声。随后，P2小目标检测头增强了模型识别小垃圾目标的功效。最后,引入了双向特征金字塔网络（BiFPN），以增强模型的深度特征融合能力。实验结果表明EcoDetect-YOLO对城市环境的适应性和优越的小目标检测能力,有效识别九种垃圾，如纸和塑料垃圾。与基线YOLOv5s模型相比，EcoDetect-YOLO的mAP0.5增长了4.7%，达到58.1%，紧凑的型号尺寸为15.7MB，FPS为39.36。值得注意的是,即使有强烈的噪音,该模型保持了超过50%的mAP0.5，强调其稳健性。总之,EcoDetect-YOLO,正如本文所提出的，拥有高精度，效率,和紧凑，使其适合部署在移动设备上，以实时检测和管理城市垃圾暴露，从而推进城市自动化治理和数字经济发展。
