HGCS-Det：一种基于深度学习的复杂场景下家庭垃圾定位与识别解决方案。

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

作者信息

Zhou Houkui, Chen Chang, Xia Zhongyi, Ding Qifeng, Liao Qinqin, Wang Qun, Yu Huimin, Hu Haoji, Zhang Guangqun, Hu Junguo, He Tao

机构信息

College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China.

Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou 311300, China.

出版信息

Sensors (Basel). 2025 Jun 14;25(12):3726. 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.

随着深度学习技术的兴起，智能垃圾检测为垃圾分类管理提供了新思路。然而，由于复杂环境的干扰，再加上垃圾不规则特征的影响，复杂场景下的垃圾检测仍面临重大挑战。此外，现有的一些研究在精度或实时性能方面存在不足，特别是应用于复杂垃圾检测场景时。因此，本文提出了一种基于YOLOv8的模型，即HGCS-Det，用于复杂场景下的垃圾检测。HGCS-Det模型设计如下：首先，引入归一化注意力模块来校准模型对目标的注意力，并抑制环境噪声干扰信息。此外，为权衡注意力特征贡献，采用注意力特征融合模块来补充各通道的注意力权重。随后，建立实例边界强化模块，通过将强梯度信息与语义信息相结合来捕捉垃圾的细粒度特征。最后，应用滑动损失函数对复杂检测环境中产生的难样本进行动态加权，以提高难样本的识别准确率。HGCS-Det仅在参数上略有增加（3.02M），在公共HGI30数据集上实现了93.6%的平均精度均值（mAP）和86帧每秒（FPS），比YOLOv12的mAP值高3.33%，在效率和适用性方面均优于当前最优（SOTA）方法。值得注意的是，HGCS-Det在增强检测精度的同时保持了轻量级架构，即使在资源受限的环境中也能实现实时性能。这些特性显著提高了其实际适用性，使该模型非常适合部署在嵌入式设备和实际垃圾分类系统中。该方法可为垃圾分类的工程应用提供有价值的技术参考。