CA-YOLO: Ensembled Model Optimization for Remote Sensing Image Object Detection

Abstract

Multiple object retrieval methods are solved by the CA-YOLO model to find elements in complex spatial images. This addresses weak multiscale feature learning and the delicate balance between model complexity and performance. CA-YOLO is based on YOLOv5 and uses a small coordinate attention module at the lower layer to recover selected features and reduce data. A fast spatial pyramid with tandem design modules at the bottom uses stochastic pooling to speed up thinking and merge features of different sizes. Anchor box interactions and missing features have been updated to make it easier to find items of different sizes and shapes. CA-YOLO outperforms YOLO in multiple object detection and average inference speed of 125 frames/sec. CA-YOLO is a great option in the same conditions and difficulties. The study also investigated V3 tiny, V4, V5s, V8s, CA Yolos, and V5x6 YOLO models. This suggests that YOLO V5x6 can achieve more than 95% mAP on remote sensing object identification datasets. Index terms - Object detection, attention mechanism, coordinate attention, SPPF, SIoU loss.