Algorithm of detection rock object in landing zone of small celestial body surface
In response to the challenging issue of indistinct surface rock contours and difficulties in detecting small-sized rocks in dim environments on small celestial bodies,a method and model for rock target detection in landing areas on small celestial body surfaces is proposed.This approach integrates a multi-head self-attention mechanism into the YOLOv8x framework to enhance the model's capability to capture the global view of images,thereby improving its adaptability to different lighting conditions in deep space environments.Additionally,a small object detection layer is added to the model to increase its focus on small-sized rocks,enhancing its adaptability to rocks of varying sizes.Comparative experimental results demonstrate that compared to the original algorithm,the proposed method achieves improvements of 6.4% in rock detection precision,3% in recall rate,and 5% in mean average precision.Furthermore,compared with other mainstream object detection algorithms,the proposed method shows significant improvements in performance metrics.This method provides a theoretical and technical foundation for the autonomous identification of rocks in landing areas on small celestial body surfaces in dim environments.
rocks detection on small body surfacedeep learningmulti-head self-attentionsmall object detectionmulti-scale feature fusion
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