ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

An Enhanced Lightweight Transformer Method Using Strong Encoder Techniques in Underwater Object Detection


Affiliations
1 Department of Electrical and Electronics Engineering, Varuvan Vadivelan Institute of Technology, India., India
2 Department of Computer Science, Kristu Jayanti College, India., India
3 Department of Civil Engineering, GRT Institute of Engineering and Technology, India., India
     

   Subscribe/Renew Journal


The two-stage lightweight transformer methodology can now be used for underwater object detection thanks to some small adjustments that have been made to it. These adjustments were made to accommodate the needs of the underwater environment. By utilising multi-scale training, making enhancements to the backbone network of the Faster RCNN, and optimising the model response to both positive and negative examples, with an emphasis on the latter, we can achieve significant gains. The enhanced lightweight transformer method has been shown to be effective through the utilisation of comparative experiments which demonstrate that the network module may be utilised in the method to serve as the feature extraction structure. This has been shown to be the case thanks to the fact that the enhanced lightweight transformer method has been shown to be effective. We first disassemble the entire system to break it down into its component components before running the detection algorithm and the ablation tests. We can perform additional tests regarding the efficiency of the object detection algorithm for lightweight transformers. When compared to the unimproved lightweight transformer technique, the F1 result is rapidly approaching 99%, which represents a substantial leap forward in terms of quality. This contributes to demonstrating that the methods that were recommended are effective.

Keywords

Lightweight Transformer, Strong Encoder, Underwater Object Detection.
Subscription Login to verify subscription
User
Notifications
Font Size

  • L. Chen and L. Wu, “Underwater Target Detection Lightweight Algorithm Based on Multi-Scale Feature Fusion”, Journal of Marine Science and Engineering, Vol. 11, No. 2, pp. 320-334, 2023.

  • Y. Sun, “Underwater Object Detection with Swin Transformer”, Proceedings of International Conference on Data Intelligence and Security, p. 422-427, 2022.

  • K. Liu and S. Tang, “Underwater Object Detection Using TC-YOLO with Attention Mechanisms”, Sensors, Vol. 23, No. 5, pp. 2567-2577, 2023.

  • K. Ali and N. Mahmood, “Marine Object Detection using Transformers”, Proceedings of International Bhurban Conference on Applied Sciences and Technology, pp. 951- 957, 2022.

  • Z. Li and Y. Gao, “Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey”, Remote Sensing, Vol. 14, No. 10, pp. 2385-2397, 2022.

  • Y. Tang, R. Sagawa and R. Furukawa, “AutoEnhancer: Transformer on U-Net Architecture search for Underwater Image Enhancement”, Proceedings of the Asian Conference on Computer Vision, pp. 1403-1420, 2022.

  • X. Xu and J. Xia, “MulTNet: A Multi-Scale Transformer Network for Marine Image Segmentation toward Fishing”, Sensors, Vol. 22, No. 19, pp. 7224-7234, 2022.

  • F. Qingyun and W. Zhaokui, “Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagery”, Pattern Recognition, Vol. 130, pp. 108786-108798, 2022.

  • S. Han and S. Zhang, “Former-CR: A Transformer-Based Thick Cloud Removal Method with Optical and SAR Imagery”, Remote Sensing, Vol. 15, No. 5, pp. 1196-1209, 2023.


Abstract Views: 106

PDF Views: 0




  • An Enhanced Lightweight Transformer Method Using Strong Encoder Techniques in Underwater Object Detection

Abstract Views: 106  |  PDF Views: 0

Authors

N. Sivakumar
Department of Electrical and Electronics Engineering, Varuvan Vadivelan Institute of Technology, India., India
A. Sumalatha
Department of Computer Science, Kristu Jayanti College, India., India
K. Prabhu
Department of Civil Engineering, GRT Institute of Engineering and Technology, India., India

Abstract


The two-stage lightweight transformer methodology can now be used for underwater object detection thanks to some small adjustments that have been made to it. These adjustments were made to accommodate the needs of the underwater environment. By utilising multi-scale training, making enhancements to the backbone network of the Faster RCNN, and optimising the model response to both positive and negative examples, with an emphasis on the latter, we can achieve significant gains. The enhanced lightweight transformer method has been shown to be effective through the utilisation of comparative experiments which demonstrate that the network module may be utilised in the method to serve as the feature extraction structure. This has been shown to be the case thanks to the fact that the enhanced lightweight transformer method has been shown to be effective. We first disassemble the entire system to break it down into its component components before running the detection algorithm and the ablation tests. We can perform additional tests regarding the efficiency of the object detection algorithm for lightweight transformers. When compared to the unimproved lightweight transformer technique, the F1 result is rapidly approaching 99%, which represents a substantial leap forward in terms of quality. This contributes to demonstrating that the methods that were recommended are effective.

Keywords


Lightweight Transformer, Strong Encoder, Underwater Object Detection.

References