YOLO-CDAW: an improved underwater garbage detection model based on YOLOv8n

Qianlai Liu; Hongmin Ren

doi:10.1117/12.3048249

3 October 2024 YOLO-CDAW: an improved underwater garbage detection model based on YOLOv8n

Qianlai Liu, Hongmin Ren

Author Affiliations +

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 1327223 (2024) https://doi.org/10.1117/12.3048249
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

Due to the complex marine environment, low visibility, object occlusion, and limited underwater resources and environment, the task of detecting marine garbage is extremely difficult. In this regard, this paper suggests an underwater garbage detection model YOLO-CDAW utilizing the improved YOLOv8n model. First, to improve the separation between underwater garbage and occluders, the CA module is inserted to the YOLOv8n model’s backbone network. Secondly, integrates the CoordConv module alongside the C2f module of the YOLOv8n model, proposes a new CD_C2f module, then utilize it to switch out some of the backbone natework’s C2f modules. This module enables the model to get full utilization of the position data relating to the object without increasing the model calculation amount and parameter amount, thereby improving the network's feature extraction ability. Finally, to strengthen the model’s capacity for generalization, the CIoU loss function is swapped out with the WIoU loss function. Experimental results show that the upgrated YOLO-CDAW model’s mAP50/% and mAP50:95/% are 2% and 1.8% greater compared to the original model for each, with an inference speed of 3.3ms and only 3.03M parameters, which fulfills the need for real-time underwater garbage detection within the limits of resource and environmental constraints.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Qianlai Liu and Hongmin Ren "YOLO-CDAW: an improved underwater garbage detection model based on YOLOv8n", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 1327223 (3 October 2024); https://doi.org/10.1117/12.3048249

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KEYWORDS

Submerged target modeling

Oceanography

Coastal modeling

Data modeling

Target detection

Environmental sensing

Performance modeling

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