YoloXT: A object detection algorithm for marine benthos

Zhang, Jianyi; Liu, Yong; Wang, Yongpan; Xu, Xianchong; Liu, Lu; Wu, Qihang

doi:10.1016/j.ecoinf.2022.101923

Cited by 14 publications

(2 citation statements)

References 13 publications

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“…The choice of YOLOv8 as the algorithm for this task was influenced by its efficient information processing capabilities and sophisticated architecture that includes advanced loss functions. It is worth noting that various versions of the YOLO algorithms have been adapted to tackle specific challenges of underwater images, such as lack of sharpness, small size, and overlap ( Zhang et al, 2022 ; Xu et al, 2023 ). The continuous evolution of these algorithms and the release of newer versions present an opportunity to test each version on the same datasets to quantify improvements in results ( Zhong et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

High-resolution density assessment assisted by deep learning of Dendrophyllia cornigera (Lamarck, 1816) and Phakellia ventilabrum (Linnaeus, 1767) in rocky circalittoral shelf of Bay of Biscay

Gayá-Vilar,

Cobo,

Abad-Uribarren

et al. 2024

PeerJ

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This study presents a novel approach to high-resolution density distribution mapping of two key species of the 1170 “Reefs” habitat, Dendrophyllia cornigera and Phakellia ventilabrum, in the Bay of Biscay using deep learning models. The main objective of this study was to establish a pipeline based on deep learning models to extract species density data from raw images obtained by a remotely operated towed vehicle (ROTV). Different object detection models were evaluated and compared in various shelf zones at the head of submarine canyon systems using metrics such as precision, recall, and F1 score. The best-performing model, YOLOv8, was selected for generating density maps of the two species at a high spatial resolution. The study also generated synthetic images to augment the training data and assess the generalization capacity of the models. The proposed approach provides a cost-effective and non-invasive method for monitoring and assessing the status of these important reef-building species and their habitats. The results have important implications for the management and protection of the 1170 habitat in Spain and other marine ecosystems worldwide. These results highlight the potential of deep learning to improve efficiency and accuracy in monitoring vulnerable marine ecosystems, allowing informed decisions to be made that can have a positive impact on marine conservation.

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Section: Discussionmentioning

confidence: 99%

High-resolution density assessment assisted by deep learning of Dendrophyllia cornigera (Lamarck, 1816) and Phakellia ventilabrum (Linnaeus, 1767) in rocky circalittoral shelf of Bay of Biscay

Gayá-Vilar,

Cobo,

Abad-Uribarren

et al. 2024

PeerJ

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“…However, the model has limitations, such as applying a single underwater dataset and slow inference speed. Similarly, Mao et al [22], Liu et al [23], and Zhang et al [24] have improved modules to reduce model size and improve detection speed. Unfortunately, little research has been conducted to effectively develop a lightweight YOLO-based target detection algorithm for underwater crabs.…”

Section: Introductionmentioning

confidence: 99%

Rethinking Underwater Crab Detection via Defogging and Channel Compensation

Sun,

Yuan,

et al. 2024

Fishes

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Crab aquaculture is an important component of the freshwater aquaculture industry in China, encompassing an expansive farming area of over 6000 km2 nationwide. Currently, crab farmers rely on manually monitored feeding platforms to count the number and assess the distribution of crabs in the pond. However, this method is inefficient and lacks automation. To address the problem of efficient and rapid detection of crabs via automated systems based on machine vision in low-brightness underwater environments, a two-step color correction and improved dark channel prior underwater image processing approach for crab detection is proposed in this paper. Firstly, the parameters of the dark channel prior are optimized with guided filtering and quadtrees to solve the problems of blurred underwater images and artificial lighting. Then, the gray world assumption, the perfect reflection assumption, and a strong channel to compensate for the weak channel are applied to improve the pixels of red and blue channels, correct the color of the defogged image, optimize the visual effect of the image, and enrich the image information. Finally, ShuffleNetV2 is applied to optimize the target detection model to improve the model detection speed and real-time performance. The experimental results show that the proposed method has a detection rate of 90.78% and an average confidence level of 0.75. Compared with the improved YOLOv5s detection results of the original image, the detection rate of the proposed method is increased by 21.41%, and the average confidence level is increased by 47.06%, which meets a good standard. This approach could effectively build an underwater crab distribution map and provide scientific guidance for crab farming.

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