HGCS-Det: A Deep Learning-Based Solution for Localizing and Recognizing Household Garbage in Complex Scenarios

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 12; p. 3726
• Main Authors: Zhou, Houkui, Chen, Chang, Xia, Zhongyi, Ding, Qifeng, Liao, Qinqin, Wang, Qun, Yu, Huimin, Hu, Haoji, Zhang, Guangqun, Hu, Junguo, He, Tao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 14.06.2025; MDPI
• Subjects: Accuracy; Algorithms; attention-feature fusion; Attentional bias; Classification; Consumption; Datasets; Deep learning; Efficiency; Embedded systems; garbage detection; instance boundary reinforcement; Localization; normalization attention; Refuse and refuse disposal; Remote sensing; Semantics; Slide Loss; China; instance boundary reinforcement; garbage detection; Slide Loss; attention-feature fusion; normalization attention
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25123726

With the rise of deep learning technology, intelligent garbage detection provides a new idea for garbage classification management. However, due to the interference of complex environments, coupled with the influence of the irregular features of garbage, garbage detection in complex scenarios still faces significant challenges. Moreover, some of the existing research suffer from shortcomings in either their precision or real-time performance, particularly when applied to complex garbage detection scenarios. Therefore, this paper proposes a model based on YOLOv8, namely HGCS-Det, for detecting garbage in complex scenarios. The HGCS-Det model is designed as follows: Firstly, the normalization attention module is introduced to calibrate the model’s attention to targets and to suppress the environmental noise interference information. Additionally, to weigh the attention-feature contributions, an Attention Feature Fusion module is employed to complement the attention weights of each channel. Subsequently, an Instance Boundary Reinforcement module is established to capture the fine-grained features of garbage by combining strong gradient information with semantic information. Finally, the Slide Loss function is applied to dynamically weight hard samples arising from the complex detection environments to improve the recognition accuracy of hard samples. With only a slight increase in parameters (3.02M), HGCS-Det achieves a 93.6% mean average precision (mAP) and 86 FPS on the public HGI30 dataset, which is a 3.33% higher mAP value than from YOLOv12, and outperforms the state-of-the-art (SOTA) methods in both efficiency and applicability. Notably, HGCS-Det maintains a lightweight architecture while enhancing the detection accuracy, enabling real-time performance even in resource-constrained environments. These characteristics significantly improve its practical applicability, making the model well suited for deployment in embedded devices and real-world garbage classification systems. This method can serve as a valuable technical reference for the engineering application of garbage classification.