Underwater Small Target Detection Based on YOLOX Combined with MobileViT and Double Coordinate Attention

Sun, Yuxin; Zheng, Wenxi; Du, Xue; Yan, Zheping

doi:10.3390/jmse11061178

Cited by 15 publications

(12 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The difference between the common convolution module and deformable convolution v2 is shown in Figure 2. The calculation formula for the output of the feature map obtained by the common convolution is shown in Equation (1). 𝑅 represents the size of the convolution kernel, it also represents the area where convolution operations can be performed on the feature map.…”

Section: Fusion Of Deformable Convolutional Feature Extraction Networkmentioning

confidence: 99%

“…The efficient use of computer vision technology to explore the unknown underwater domain is one of the most active research fields for many researchers. Due to the dynamic and changeable underwater visual environment, we must promote visual recognition tracking and dynamic perception algorithms to deal with the complex challenges [1,2]. Effectively utilizing these resources can help prevent the overexploitation and destruction of terrestrial resources.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

Liu,

Huang,

Duan

et al. 2023

Electronics

View full text Add to dashboard Cite

Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and marine resource surveying. However, the complex underwater environment, including factors such as light changes and background noise, poses a significant challenge to target detection. We propose an improved underwater target detection algorithm based on YOLOv8n to overcome these problems. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region’s features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIoU loss function with the Wise-IoU loss function. We named our new algorithm DSW-YOLOv8n, which is an acronym of Deformable Convnets v2, SimAm, and Wise-IoU of the improved YOLOv8n(DSW-YOLOv8n). To conduct our experiments, we created our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on underwater target detection were 88.6% and 51.8%, respectively, and the DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 91.8% and 55.9%. The original YOLOv8n algorithm was 62.2% and 45.9% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 65.7% and 48.3%, respectively. The number of parameters of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method.

show abstract

Section: Fusion Of Deformable Convolutional Feature Extraction Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

Liu,

Huang,

Duan

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…The application of computer vision technology for exploring the hitherto uncharted underwater realm constitutes one of the most dynamically advancing research frontiers [1,2]. Fish detection in underwater object detection tasks has been a focal point of attention [3].…”

Section: Introductionmentioning

confidence: 99%

DyFish-DETR: Underwater Fish Image Recognition Based on Detection Transformer

Wang,

Ruan,

Chen

2024

JMSE

View full text Add to dashboard Cite

Due to the complexity of underwater environments and the lack of training samples, the application of target detection algorithms to the underwater environment has yet to provide satisfactory results. It is crucial to design specialized underwater target recognition algorithms for different underwater tasks. In order to achieve this goal, we created a dataset of freshwater fish captured from multiple angles and lighting conditions, aiming to improve underwater target detection of freshwater fish in natural environments. We propose a method suitable for underwater target detection, called DyFish-DETR (Dynamic Fish Detection with Transformers). In DyFish-DETR, we propose a DyFishNet (Dynamic Fish Net) to better extract fish body texture features. A Slim Hybrid Encoder is designed to fuse fish body feature information. The results of ablation experiments show that DyFishNet can effectively improve the mean Average Precision (mAP) of model detection. The Slim Hybrid Encoder can effectively improve Frame Per Second (FPS). Both DyFishNet and the Slim Hybrid Encoder can reduce model parameters and Floating Point Operations (FLOPs). In our proposed freshwater fish dataset, DyFish-DETR achieved a mAP of 96.6%. The benchmarking experimental results show that the Average Precision (AP) and Average Recall (AR) of DyFish-DETR are higher than several state-of-the-art methods. Additionally, DyFish-DETR, respectively, achieved 99%, 98.8%, and 83.2% mAP in other underwater datasets.

show abstract

“…Underwater robots rely on underwater video footage or images for underwater litter detection [12]. Common methods for video and image acquisition include satellite remote sensing, sonar, and optical cameras.…”

Section: Introductionmentioning

confidence: 99%

YOLOv7t-CEBC Network for Underwater Litter Detection

Zhang,

Zhu,

Gan

2024

JMSE

View full text Add to dashboard Cite

The issue of marine litter has been an important concern for marine environmental protection for a long time, especially underwater litter. It is not only challenging to clean up, but its prolonged presence underwater can cause damage to marine ecosystems and biodiversity. This has led to underwater robots equipped with powerful visual detection algorithms becoming the mainstream alternative to human labor for cleaning up underwater litter. This study proposes an enhanced underwater litter detection algorithm, YOLOv7t-CEBC, based on YOLOv7-tiny, to assist underwater robots in target identification. The research introduces some modules tailored for marine litter detection within the model framework, addressing inter-class similarity and intra-class variability inherent in underwater waste while balancing detection precision and speed. Experimental results demonstrate that, on the Deep Plastic public dataset, YOLOv7t-CEBC achieves a detection accuracy (mAP) of 81.8%, markedly surpassing common object detection algorithms. Moreover, the detection frame rate reaches 118 FPS, meeting the operational requirements of underwater robots. The findings affirm that the enhanced YOLOv7t-CEBC network serves as a reliable tool for underwater debris detection, contributing to the maintenance of marine health.

show abstract

Underwater Small Target Detection Based on YOLOX Combined with MobileViT and Double Coordinate Attention

Cited by 15 publications

References 33 publications

DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

DyFish-DETR: Underwater Fish Image Recognition Based on Detection Transformer

YOLOv7t-CEBC Network for Underwater Litter Detection

Contact Info

Product

Resources

About