An Underwater Image Color Correction Algorithm Based on Underwater Scene Prior and Residual Network

Huang, Mengxing; Ye, Jinjin; Zhu, Shenghan; Chen, Yang; Wu, Yuanyuan; Wu, Di; Feng, Siling; Feng, Shuyi

doi:10.1007/978-3-031-06788-4_11

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Due to the complex and variable nature of the underwater environment, collected underwater images often suffer from issues such as blurriness, color distortion, and low contrast [20][21][22][23][24]. These conditions can significantly impede underwater target detection tasks.…”

Section: Introductionmentioning

confidence: 99%

A Method for Underwater Biological Detection Based on Improved YOLOXs

Wang,

Zhang,

You

et al. 2024

Applied Sciences

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This article proposes a lightweight underwater biological target detection network based on the improvement of YOLOXs, addressing the challenges of complex and dynamic underwater environments, limited memory in underwater devices, and constrained computational capabilities. Firstly, in the backbone network, GhostConv and GhostBottleneck are introduced to replace standard convolutions and the Bottleneck1 structure in CSPBottleneck_1, significantly reducing the model’s parameter count and computational load, facilitating the construction of a lightweight network. Next, in the feature fusion network, a Contextual Transformer block replaces the 3 × 3 convolution in CSPBottleneck_2. This enhances self-attention learning by leveraging the rich context between input keys, improving the model’s representational capacity. Finally, the positioning loss function Focal_EIoU Loss is employed to replace IoU Loss, enhancing the model’s robustness and generalization ability, leading to faster and more accurate convergence during training. Our experimental results demonstrate that compared to the YOLOXs model, the proposed YOLOXs-GCE achieves a 1.1% improvement in mAP value, while reducing parameters by 24.47%, the computational load by 26.39%, and the model size by 23.87%. This effectively enhances the detection performance of the model, making it suitable for complex and dynamic underwater environments, as well as underwater devices with limited memory. The model meets the requirements of underwater target detection tasks.

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Section: Introductionmentioning

confidence: 99%

A Method for Underwater Biological Detection Based on Improved YOLOXs

Wang,

Zhang,

You

et al. 2024

Applied Sciences

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“…Nevertheless, in the challenging underwater environment, the obtained image still suffers from different degrees of degradation, such as color distortion [6][7][8][9][10], low light and contrast [11,12], and haze-like effects [11][12][13][14][15][16][17][18][19]. The original YOLOv5 model is easily affected by the above problems.…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv5 Underwater Detector Based on an Attention Mechanism and Multi-Branch Reparameterization Module

et al. 2023

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Underwater target detection is a critical task in various applications, including environmental monitoring, underwater exploration, and marine resource management. As the demand for underwater observation and exploitation continues to grow, there is a greater need for reliable and efficient methods of detecting underwater targets. However, the unique underwater environment often leads to significant degradation of the image quality, which results in reduced detection accuracy. This paper proposes an improved YOLOv5 underwater-target-detection network to enhance accuracy and reduce missed detection. First, we added the global attention mechanism (GAM) to the backbone network, which could retain the channel and spatial information to a greater extent and strengthen cross-dimensional interaction so as to improve the ability of the backbone network to extract features. Then, we introduced the fusion block based on DAMO-YOLO for the neck, which enhanced the system’s ability to extract features at different scales. Finally, we used the SIoU loss to measure the degree of matching between the target box and the regression box, which accelerated the convergence and improved the accuracy. The results obtained from experiments on the URPC2019 dataset revealed that our model achieved an mAP@0.5 score of 80.2%, representing a 1.8% and 2.3% increase in performance compared to YOLOv7 and YOLOv8, respectively, which means our method achieved state-of-the-art (SOTA) performance. Moreover, additional evaluations on the MS COCO dataset indicated that our model’s mAP@0.5:0.95 reached 51.0%, surpassing advanced methods such as ViDT and RF-Next, demonstrating the versatility of our enhanced model architecture.

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An Underwater Image Color Correction Algorithm Based on Underwater Scene Prior and Residual Network

Cited by 2 publications

References 22 publications

A Method for Underwater Biological Detection Based on Improved YOLOXs

A Method for Underwater Biological Detection Based on Improved YOLOXs

An Improved YOLOv5 Underwater Detector Based on an Attention Mechanism and Multi-Branch Reparameterization Module

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