Lightweight Surface Litter Detection Algorithm Based on Improved YOLOv5s
Source abstract
Abstract
In response to the problem of the high cost and low efficiency of traditional water surface litter cleanup through manpower, a lightweight water surface litter detection algorithm based on improved YOLOv5s is proposed to prov... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Water Quality Monitoring Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (40)
Show source-provided references
1. China is implementing “Garbage classification” action10.1016/j.envpol.2019.01.013 ↗
2. IWSCR: An intelligent water surface cleaner robot for collecting floating garbage10.1109/TSMC.2019.2961687 ↗
3. Design and implementation of small waters intelligent garbage cleaning robot system based on raspberry pi
4. LocateUAV: Unmanned aerial vehicle location estimation via contextual analysis in an IoT environment10.1109/JIOT.2022.3162300 ↗
5. Research on image target detection algorithm based on depth learning
6. Lite-hrnet: A lightweight high-resolution network
7. An elevator button recognition method combining yolov5 and ocr10.32604/cmc.2023.033327 ↗
8. Rich feature hierarchies for accurate object detection and semantic segmentation
9. Fast R-CNN
10. Faster R-CNN: Towards real-time object detection with region proposal networks
11. You only look once: Unified, real-time object detection
12. YOLO9000: Better, faster, stronger
13. SSD: Single shot multibox detector
14. DSOD: Learning deeply supervised object detectors from scratch
15. Deep neural network driven automated underwater object detection10.32604/cmc.2022.021168 ↗
16. CBAM: Convolutional block attention module
17. Y. Zhang, W. Wen, Z. Zhang, Z. Jia, L. Wang et al., “Focal and efficient IoU loss for accurate bounding box regression,” arXiv preprint, pp. 146–157, 2021. https://arxiv.org/pdf/2101.0815810.1016/j.neucom.2022.07.042 ↗
18. Z. Zheng, P. Wang, W. Liu, J. Li, R. Ye et al., “Distance-IoU loss: Faster and better learning for bounding box regression,” arXiv preprint, pp. 12993–13000, 2019. https://arxiv.org/abs/1911.08287v110.1609/aaai.v34i07.6999 ↗
19. Ship image recognition using HOG
20. Examination of automatic detection and tracking of ships on camera image in marine environment
21. Real-time water surface object detection based on improved faster R-CNN
22. AquaVision: Automating the detection of waste in water bodies using deep transfer learning10.1016/j.cscee.2020.100026 ↗
23. A modified YOLOv3 detection method for vision-based water surface garbage capture robot10.1177/1729881420932715 ↗
24. Ultralytics/yolov5: V5.0-YOLOv5-p6 models, AWS, supervisely and YouTube integrations10.5281/zenodo.4679653 ↗
25. Feature pyramid networks for object detection
26. Path aggregation network for instance segmentation
27. Survey of deep learning model compression and acceleration
28. Survey of lightweight netural network
29. F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally et al., “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size,” arXiv preprint, 2016. https://arvix.org/pdf/1602.07360
30. ShuffleNet: An extremely efficient convolutional neural network for mobile devices
31. ShuffleNet v2: Practical guidelines for efficient cnn architecture design
32. A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint, 2017. https://arvix.org/abs/1704.04861
33. Mobilenetv2: Inverted residuals and linear bottlenecks
34. Searching for mobilenetv3
35. X. Jiang, “Research on scene image classification algorithm based on deep learning,” M. S. Dissertation, Beijing University of Posts and Telecommunications, China, 2019.
36. SNAS: Stochastic neural architecture search
37. Squeeze-and-excitation networks
38. H. Zhang, M. Cisse, Y. N. Dauphin and D. Lopez-Paz, “Mixup: Beyond empirical risk minimization,” arXiv preprint, 2017. https://arxiv.org/abs/1710.09412
39. GhostNet: More features from cheap operations
40. Research on hierarchical decomposition of convolution neural network