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A Lightweight Underwater Object Detection Algorithm with Adaptive Image Enhancement Based on YOLOv8

A Lightweight Underwater Object Detection Algorithm with Ada...

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第43届中国控制会议

作者： Zuxin Zhao Jiarong Han Zhongjing Ma Suli Zou Yu Liu Guancheng Li Institute of Acoustics China Academy of Sciences China Changfeng Electromechanical Technology research and Design Institute

This paper introduces a lightweight underwater object detection algorithm based on YOLOv8, essential for underwater robotics challenged by environmental complexity and real-time demands. Firstly, to enhance underwater image quality without significantly increasing computational demands, this study introduces an Adaptive Underwater Image Enhancement module utilizing lightweight convolutions and digital filters for dynamic enhancement. Secondly, a re-parameterized Partial Convolution Block is proposed and integrated, replacing foundational blocks in the baseline model's architecture, resulting in reduced detection network parameters and enhanced accuracy. Additionally, performance evaluation on the UTDAC dataset demonstrates our model achieving a 46.8% mAP, marking a 1.6% improvement over the baseline, with a total parameter count of merely *** studies and extended experiments validate the effectiveness and adaptability of the proposed modules. Experimental results show that the model achieves a superior balance between accuracy and processing speed, making it particularly suitable for underwater robotic perception.

关键词： Underwater Object Detection Underwater Image Enhancement YOLOv8 structural re-parameterization

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A Novel Approach of Driver Facial Expression recognition Based on Improved Swin Transformer

A Novel Approach of Driver Facial Expression Recognition Bas...

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第43届中国控制会议

作者： Peng Qi Zhengguang Liu Mohammad Asadpourhafshejani Dezheng Hua Xinhua Liu School of Mechatronic Engineering China University of Mining and Technology

Driver facial expression recognition(FER) is a fundamental yet challenging task for the perception system of intelligent *** is difficult for the current recognition model to achieve high accuracy and fast *** overcome the above issue,we propose a novel driver facial expression recognition method based on an improved Swin ***,shallow features of facial images are extracted through CNNs to reduce the patch of image sequences and lower the computational complexity of self-attention ***,the residual coordinate attention mechanism is designed to focus on crucial facial features and suppress environmental ***,the structural re-parameterization method is employed to accelerate the model while maintaining recognition *** are conducted on three widely used expression datasets,and the results demonstrate that our approach is competitive to several state-of-the-art methods in FER *** experimental results evidence the practicality of the RCST,and the overall accuracy reaches 95.04% on the HDER dataset.

关键词： Driver facial expression recognition Swin Transformer structural re-parameterization Attention mechanism

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