題目：High-Resolution Network with Attention Mechanism for Shrimp Larvae Counting
Aquaculture is a vital sector in global food production, with shrimp farming being a key branch within it. Accurate individual counting of shrimp fry is crucial for effective aquaculture management. However, traditional manual counting methods are time-consuming, labor-intensive, and prone to errors. Additionally, challenges such as mutual occlusion and varying lighting conditions in densely populated areas of shrimp fry can lead to inaccurate counting. In this paper, we propose the use of a high-resolution network, HRNET, to extract important information from images at multiple scales, mitigating information loss caused by external factors like lighting and overlapping due to mutual occlusion. By incorporating attention mechanisms, we enhance the feature representation of the backbone network for small targets, thereby improving recognition and counting accuracy. Furthermore, to address differences between dense and sparse regions, we employ dilated convolutions to expand the receptive field and replace the mean squared error (MSE) loss, which solely focuses on pixel-level average errors, with the DMS-SSIM loss function that considers similarity in subtle image structures and quality. Experimental results demonstrate a significant improvement in both mean absolute error (MAE) and MSE.
龍偉，博士，碩導，湖州師範學院信追踪之靴息工程學院教師，同濟大學電子與多宝体育app下载訪問學者。主要研究智能信息處理、計算機視覺與感知』智能、人機共融與智能應用等方向，目前在智慧水產呵欠養殖信息化方面有著較深的研究。先後主持及參與了國家自然科學基金、省級重】點研發項目、省級自然科◤學基金及市級重點研發項目等課徐晓晨題，在Molecules、Frontiers in Psychology、Multimedia Tools and Applications、International Conference on Networking Systems of AI、《江蘇農業學報》等國內外期刊和會議上發表高水平SCI、EI論文20多篇，發明專利、軟件著作權共計20余項。