YOLOV4_CSPBi: Enhanced Land Target Detection Model
نویسندگان
چکیده
The identification of small land targets in remote sensing imagery has emerged as a significant research objective. Despite advancements object detection strategies based on deep learning for visible images, the performance detecting and densely distributed number remains suboptimal. To address this issue, study introduces an improved model named YOLOV4_CPSBi, YOLOV4 architecture, specifically designed to enhance capability imagery. proposed enhances traditional CSPNet by redefining its channel partitioning integrating enhanced structure into neck part YOLO network model. Additionally, conventional pyramid fusion used BiFPN is removed. By weight-based bidirectional multi-scale mechanism feature fusion, capable effectively reasoning about objects various sizes, with particular focus targets, without introducing increase computational costs. Using DOTA dataset data, quantifies Compared baseline models, AP been nearly 8% compared YOLOV4. combining these modifications, demonstrates promising results identifying images.
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ژورنال
عنوان ژورنال: Land
سال: 2023
ISSN: ['2073-445X']
DOI: https://doi.org/10.3390/land12091813