Lightweight Underwater Target Detection Using YOLOv8 with Multi-Scale Cross-Channel Attention
Source abstract
Abstract
Underwater target detection is extensively applied in domains such as underwater search and rescue, environmental monitoring, and marine resource surveys. It is crucial in enabling autonomous underwater robot operations and p... | 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 · Image Enhancement Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (37)
Show source-provided references
1. Multi-dimensional, multi-functional and multi-level attention in YOLO for underwater object detection10.1007/s00521-023-08781-w ↗
2. A systematic review and analysis of deep learning-based underwater object detection10.1016/j.neucom.2023.01.056 ↗
3. PE-Transformer: Path enhanced transformer for improving underwater object detection10.1016/j.eswa.2024.123253 ↗
4. A comprehensive survey of the R-CNN family for object detection
5. SSD real-time illegal parking detection based on contextual information transmission
6. YOLOv1 to v8: Unveiling each variant—A comprehensive review of YOLO10.1109/ACCESS.2024.3378568 ↗
7. A comprehensive review of YOLO architectures in computer vision: From YOLOv1 to YOLOv8 and YOLO-NAS10.3390/make5040083 ↗
8. YOLOv8: A novel object detection algorithm with enhanced performance and robustness
9. FEB-YOLOv8: A multi-scale lightweight detection model for underwater object detection
10. Underwater object detection in marine ranching based on improved YOLOv810.3390/jmse12010055 ↗
11. Comparative investigations on tomato leaf disease detection and classification using CNN, R-CNN, Fast R-CNN and faster R-CNN
12. Two-stage underwater object detection network using swin transformer10.1109/ACCESS.2022.3219592 ↗
13. Underwater target detection based on faster R-CNN and adversarial occlusion network10.1016/j.engappai.2021.104190 ↗
14. ROIMIX: Proposal-fusion among multiple images for underwater object detection
15. Underwater biological detection algorithm based on improved faster-RCNN10.3390/w13172420 ↗
16. Underwater object detection method based on improved faster RCNN
17. Boosting R-CNN: Reweighting R-CNN samples by RPN’s error for underwater object detection10.1016/j.neucom.2023.01.088 ↗
18. Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network10.1016/j.compag.2021.106135 ↗
19. Z. Jiang and R. Wang, “Underwater object detection based on improved single shot MultiBox detector,” presented at the Proc. 2020 3rd Int. Conf. Algorithms Comput. Artif. Intell., Sanya, China, 2021. doi: 10.1145/3446132.3446170.10.1145/3446132.3446170 ↗
20. Underwater sea cucumbers detection based on improved SSD
21. Underwater object detection based on improved SSD with convolutional block attention
22. Underwater object detection using Invert Multi-Class Adaboost with deep learning
23. A. Bochkovskiy, C. -Y. Wang, and H. -Y. M. J. A. Liao, “YOLOv4: Optimal speed and accuracy of object detection,” 2020. doi: 10.48550/arXiv.2004.10934.
24. Underwater object detection using collaborative weakly supervision10.1016/j.compeleceng.2022.108159 ↗
25. G. Jocher et al., “Yolov5,” 2021. Accessed: Aug. 15, 2024. [Online]. Available: https://github.com/ultralytics/yolov5
26. Small object detection algorithm based on context information and attention mechanism
27. An efficient YOLO network with CSPCBAM, ghost, and cluster-NMS for underwater target detection10.1109/ACCESS.2024.3368878 ↗
28. DC-YOLOv8: Small-size object detection algorithm based on camera sensor10.3390/electronics12102323 ↗
29. RepVGG: Making VGG-style ConvNets great again
30. RCS-YOLO: A fast and high-accuracy object detector for brain tumor detection
31. SegNeXt: Rethinking convolutional attention design for semantic segmentation
32. Strip pooling: Rethinking spatial pooling for scene parsing
33. Large Kernel matters—Improve semantic segmentation by global convolutional network
34. National Natural Science Foundation of China. “Underwater robot picking contest,” 2017. Accessed: Sep. 15, 2024. [Online]. Available: http://2017.urpc.org.cn/index.html
35. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
36. BiFormer: Vision transformer with bi-level routing attention
37. DLCH-YOLO: An object detection algorithm for monitoring the operation status of circuit breakers in power scenarios