【摘要】 针对暗弱环境下小天体表面岩石轮廓特征不明显及岩石尺寸小而造成的难检测问题,提出了一种小天体表面着陆区岩石目标检测方法及模型。将多头自注意力机制融入YOLOv8x框架,用于提高模型获取图片全局视野的能力,增强模型对深空环境中不同光照条件下岩石特征的自适应性;在此基础上增加小目标检测层,用于提升模型对小尺寸岩石的关注度,增强模型对不同尺寸岩石的自适应性。对比实验结果表明,方法相较于改进前算法,岩石检测准确率、召回率和平均检测精度分别提升了6.4%、3%、5%,与其他主流目标检测算法相比,指标也得到明显提升。该方法为暗弱环境下小天体表面着陆区岩石的自主识别提供了理论和技术基础。更多还原

【Abstract】 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.更多还原