Abstract
To enhance the cleanliness of creek environments, quadruped robots can be utilized to detect for creek waste. The continuous changes in the water environment significantly reduce the accuracy of image detection when using quadruped robots for image acquisition. In order to improve the accuracy of quadruped robots in waste detection, this article proposed a detection model called YOLOv7-GCM model for creek waste. The model integrated a global attention mechanism (GAM) into the YOLOv7 model, which achieved accurate waste detection in ever-changing backgrounds and underwater conditions. A content-aware reassembly of features (CARAFE) replaced a up-sampling of the YOLOv7 model to achieve more accurate and efficient feature reconstruction. A minimum point distance intersection over union (MPDIOU) loss function replaced the CIOU loss function of the YOLOv7 model to more accurately measure the similarity between target boxes and predictive boxes. After the aforementioned improvements, the YOLOv7-GCM model was obtained. A quadruped robot to patrol the creek and collect images of creek waste. Finally, the YOLOv7-GCM model was trained on the creek waste dataset. The outcomes of the experiment show that the precision rate of the YOLOv7-GCM model has increased by 4.2% and the mean average precision (mAP@0.5) has accumulated by 2.1%. The YOLOv7-GCM model provides a new method for identifying creek waste, which may help promote efficient waste management.








Similar content being viewed by others
Data availability
The code and data supporting this research are stored in the Science Data Bank of generalist data repository, and the access link is https://www.scidb.cn/en/s/6FrUJf.
References
Plötz T, Guan Y (2018) Deep learning for human activity recognition in mobile computing. Computer 51(5):50–59. https://doi.org/10.1109/MC.2018.2381112
Mittal P, Singh R, Sharma A (2020) Deep learning-based object detection in low-altitude UAV datasets: a survey. Image Vis Comput 104(3):104046. https://doi.org/10.1016/j.imavis.2020.104046
Chen X, Li J (2019) Research on an Efficient Single-Stage Multi-object Detection Algorithm. In: 2019 International Conference on Smart Grid and Electrical Automation (ICSGEA). https://doi.org/10.1109/ICSGEA.2019.00110
Kumar A, Kalia A, Verma K, Sharma A, Kaushal M (2021) Scaling up face masks detection with YOLO on a novel dataset. Optik 239:166744. https://doi.org/10.1016/j.ijleo.2021.166744
ALEXEY B, WANG C, LIAO H. YOLOv4: Optimal speed and accuracy of object detection, https://arxiv.org/abs/2004.10934
Gai R, Chen N, Yuan H (2023) A detection algorithm for cherry fruits based on the improved YOLO-v4 model. Neural Comput Applic 35:13895–13906. https://doi.org/10.1007/s00521-021-06029-z
Al Muksit A, Hasan F, Emon MF, Haque MR, Anwary AR, Shatabda S (2022) YOLO-Fish: A robust fish detection model to detect fish in realistic underwater environment. Ecol Informatics 72:101847. https://doi.org/10.1016/j.ecoinf.2022.101847
Girshick R, Donahue J, Darrell T et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Comput Soc. https://doi.org/10.1109/CVPR.2014.81
Ren S, He K, Girshick R et al (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Analy Machine Intell 39(6):1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031
Cheng X, Fei H, Song L, Zhu J, Ming Z, Wang C, Yang L, Ruan Y (2023) A novel recyclable garbage detection system for waste-to-energy based on optimized CenterNet with feature fusion. J Signal Process Syst 95(1):67–76. https://doi.org/10.1007/s11265-022-01811-1
Tian M, Xiali LI, Kong S et al (2022) A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot. J Front Inf Electron Eng 23:12
Hou C, Guan Z, Guo Z et al (2023) An improved YOLOv5s based scheme for target detection in a complex underwater environment. J Marine Sci Eng 11:1041
Zhang Q, Chang X, Meng Z et al (2021) Equipment detection and recognition in electric power room based on faster R-CNN. J Procedia Comput Sci 183:324–330. https://doi.org/10.1016/J.PROCS.2021.02.066
Abdulghani AM, Abdulghani MM, Walters WL et al (2023) Multiple data augmentation strategy for enhancing the performance of YOLOv7 object detection algorithm. J Tech Sci Press. https://doi.org/10.32604/JAI.2023.041341
Shamsuzzaman JM (2022) YOLObin: non-decomposable garbage identification and classification based on YOLOv7. J Comput Commun 10:104–121
Stancilas S, Pathinarupothi RK, Gopalakrishnan U (2013) Detection of Pathological Markers in Colonoscopy Images using YOLOv7. In: 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS).0 [2023–12–29], https://doi.org/10.1109/ICICCS56967.2023.10142724
Wang S, Wu D, Zheng X (2023) TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection. Front Plant Sci 14:1223410. https://doi.org/10.3389/fpls.2023.1223410
Sun Y, Zhang S, Shi Y, Tang F, Chen J, Xiong Y, Dai Y, Li L (2024) "YOLOv7-DCN-SORT: An algorithm for detecting and counting targets on Acetes fishing vessel operation. Fisheries Res 274:106983
Wang J, Chen K, Rui X, Liu Z, Loy CC, Lin D (2021) CARAFE++: unified content-aware ReAssembly of FEatures. IEEE Trans Pattern Analy Machine Intell. https://doi.org/10.1109/TPAMI.2021.3074370
An K, Duanmu H, Zhiyang W, Liu Y, Qiao J, Shangguan Q, Song Y, Xiaonong X (2024) Enhancing small object detection in aerial images: a novel approach with PCSG model. Aerospace 11:392
Kim TK, Kim JS, Cho HC (2023) Deep-learning-based gestational sac detection in ultrasound images using modified YOLOv7-E6E model. J Animal Sci Technol 65:627–637. https://doi.org/10.5187/JAST.2023.E43
Zheng Z, Wang P, Ren D et al (2021) Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE. https://doi.org/10.1109/TCYB.2021.3095305
Duan K, Xie L, Qi H et al. (2021) Location-Sensitive Visual Recognition with Cross-IOU Loss. https://doi.org/10.48550/arXiv.2104.04899
Liu X, Gan H, Yan Y (2021) Study on improvement of YOLOv3 algorithm. J. Phys Conf Series 1884:012031. https://doi.org/10.1088/1742-6596/1884/1/012031
Jin Q, Han Q, Su N et al (2023) A deep learning and morphological method for concrete cracks detection. J Circuits Syst Comput. https://doi.org/10.1142/S0218126623502717
Konala TR, Nammi A, Tella DS (2023) Analysis of Live Video Object Detection using YOLOv5 and YOLOv7. In: 4th International Conference for Emerging Technology (INCET).0 [2023–12–29]. https://doi.org/10.1109/INCET57972.2023.10169926
Modha DS, Akopyan F et al (2023) Neural inference at the frontier of energy, space, and time. Science 382:329–335. https://doi.org/10.1126/science.adh1174
Gang X, Yue Q, Liu X (2023) Realtime monitoring of concrete crack based on deep learning algorithms and image processing techniques. Adv Eng Inf 58:102214
Song Z, Huang X, Ji C, Zhang Y (2023) Intelligent identification method of hydrophobic grade of composite insulator based on efficient - former network. IEEJ Trans Electr Electron Eng 18:1160
Li Z, Zhu Y, Sui S, Zhao Y, Liu P, Li X (2024) Real-time detection and counting of wheat ears based on improved YOLOv7. Comput Electron Agricul 218:108670
Acknowledgements
This project is sponsored by the Natural Science Foundation of Guangxi Zhuang Autonomous Region (2021GXNSFAA220091) and the Wuzhou Central Leading Local Science and Technology Development Fund Project Grant No. 202201001.
Author information
Authors and Affiliations
Contributions
Jianhua Qin: conceptualization Honglan Zhou: writing Huaian Yi: supervision Luyao Ma: data curation Jianhan Nie: resources Tingting Huang: visualization.
Corresponding author
Ethics declarations
conflict of Interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Qin, J., Zhou, H., Yi, H. et al. YOLOv7-GCM: a detection algorithm for creek waste based on improved YOLOv7 model. Pattern Anal Applic 27, 116 (2024). https://doi.org/10.1007/s10044-024-01338-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10044-024-01338-0