Smart City Gnosys
Smart city article details
| Title | A Domestic Trash Detection Model Based On Improved Yolox |
|---|---|
| ID_Doc | 1560 |
| Authors | Liu C.; Xie N.; Yang X.; Chen R.; Chang X.; Zhong R.Y.; Peng S.; Liu X. |
| Year | 2022 |
| Published | Sensors, 22, 18 |
| DOI | http://dx.doi.org/10.3390/s22186974 |
| Abstract | Domestic trash detection is an essential technology toward achieving a smart city. Due to the complexity and variability of urban trash scenarios, the existing trash detection algorithms suffer from low detection rates and high false positives, as well as the general problem of slow speed in industrial applications. This paper proposes an i-YOLOX model for domestic trash detection based on deep learning algorithms. First, a large number of real-life trash images are collected into a new trash image dataset. Second, the lightweight operator involution is incorporated into the feature extraction structure of the algorithm, which allows the feature extraction layer to establish long-distance feature relationships and adaptively extract channel features. In addition, the ability of the model to distinguish similar trash features is strengthened by adding the convolutional block attention module (CBAM) to the enhanced feature extraction network. Finally, the design of the involution residual head structure in the detection head reduces the gradient disappearance and accelerates the convergence of the model loss values allowing the model to perform better classification and regression of the acquired feature layers. In this study, YOLOX-S is chosen as the baseline for each enhancement experiment. The experimental results show that compared with the baseline algorithm, the mean average precision (mAP) of i-YOLOX is improved by 1.47%, the number of parameters is reduced by 23.3%, and the FPS is improved by 40.4%. In practical applications, this improved model achieves accurate recognition of trash in natural scenes, which further validates the generalization performance of i-YOLOX and provides a reference for future domestic trash detection research. © 2022 by the authors. |
| Author Keywords | attention mechanism; deep learning; domestic trash; object detection; YOLOX |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
58438  | 0.889 | Zhou P.; Zhu Z.; Xu X.; Liu X.; He B.; Zhang J. | Towards The Urban Future: A Novel Trash Segregation Algorithm Based On Improved Yolov4 | 2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021 (2021) |
| 16339 | 0.88 | Dong S.; Xu W.; Zhang H.; Gong L. | Cot-Dcn-Yolo: Self-Attention-Enhancing Yolov8S For Detecting Garbage Bins In Urban Street View Images | Egyptian Journal of Remote Sensing and Space Science, 28, 1 (2025) |
| 11208 | 0.88 | Kavitha R.; Yazhini S. | Automated Garbage Waste Management Using Deep Learning For Sustainability | 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2025 (2025) |
| 58937 | 0.876 | Jadhav R.; Mhaske A.; Nawale A.; Oswal M.; Mulhar S. | Trashintelai- Garbage Collection Automation Using Deep Learning And Image Processing | 2023 Global Conference on Information Technologies and Communications, GCITC 2023 (2023) |
| 22313 | 0.872 | Chauhan R.; Shighra S.; Madkhali H.; Nguyen L.; Prasad M. | Efficient Future Waste Management: A Learning-Based Approach With Deep Neural Networks For Smart System (Lads) | Applied Sciences (Switzerland), 13, 7 (2023) |
| 58934 | 0.87 | Sultana R.; Adams R.D.; Yan Y.; Yanik P.M.; Tanaka M.L. | Trash And Recycled Material Identification Using Convolutional Neural Networks (Cnn) | Conference Proceedings - IEEE SOUTHEASTCON, 2020-March (2020) |
| 17894 | 0.868 | Vivekanandan M.S.; Jesudas T. | Deep Learning Implemented Visualizing City Cleanliness Level By Garbage Detection | Intelligent Automation and Soft Computing, 36, 2 (2023) |
| 17970 | 0.866 | Alsubaei F.S.; Al-Wesabi F.N.; Hilal A.M. | Deep Learning-Based Small Object Detection And Classification Model For Garbage Waste Management In Smart Cities And Iot Environment | Applied Sciences (Switzerland), 12, 5 (2022) |
| 62113 | 0.866 | De Carolis B.; Ladogana F.; MacChiarulo N. | Yolo Trashnet: Garbage Detection In Video Streams | IEEE Conference on Evolving and Adaptive Intelligent Systems, 2020-May (2020) |
| 23450 | 0.864 | Yang L.; Zha X.; Huang J.; Liu Z.; Chen J.; Mou C. | Energy-Efficient And Comprehensive Garbage Bin Overflow Detection Model Based On Spiking Neural Networks | Smart Cities, 8, 2 (2025) |