High-resolution remote sensing images play an important role in urban decision-making, natural resource supervision and environmental assessment, etc. To meet the demands of such fields, deep detection methods such as FasterRCNN and YOLOv5 were deeply analyzed. Multi-scale feature fusion was achieved using FPN, attention expression was enhanced through Transformer and CBAM, and detection efficiency was improved through MobileNet and pruning techniques. In terms of segmentation, the semantic description of spatial information is highlighted by using dilated convolution and jump connection. Multimodal fusion can improve the recognition accuracy of complex ground objects. The semantic model ability is enhanced through the Transformer module. Finally, a new edge detection branch is proposed to improve the contour extraction effect.

Chuying Lu. Object Detection and Image Segmentation Algorithm Optimization in High-Resolution Remote Sensing Images. International Journal of Multimedia Computing (2025), Vol. 6, Issue 1: 144-151. https://doi.org/10.38007/IJMC.2025.060114.

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