Focused on the task of fast and accurate armored target detection in ground battlefield,a detection method based on multi-scale representation network(MS-RN) and shape-fixed Guided Anchor(SF-GA)scheme is proposed.Firs...Focused on the task of fast and accurate armored target detection in ground battlefield,a detection method based on multi-scale representation network(MS-RN) and shape-fixed Guided Anchor(SF-GA)scheme is proposed.Firstly,considering the large-scale variation and camouflage of armored target,a new MS-RN integrating contextual information in battlefield environment is designed.The MS-RN extracts deep features from templates with different scales and strengthens the detection ability of small targets.Armored targets of different sizes are detected on different representation features.Secondly,aiming at the accuracy and real-time detection requirements,improved shape-fixed Guided Anchor is used on feature maps of different scales to recommend regions of interests(ROIs).Different from sliding or random anchor,the SF-GA can filter out 80% of the regions while still improving the recall.A special detection dataset for armored target,named Armored Target Dataset(ARTD),is constructed,based on which the comparable experiments with state-of-art detection methods are conducted.Experimental results show that the proposed method achieves outstanding performance in detection accuracy and efficiency,especially when small armored targets are involved.展开更多
最小临床意义差值(minimal clinically important difference,MCID)的常用估算方法包括效标法、分布法和文献分析法等。目前MCID的估算方法众多且不统一,给MCID的确定、结果解释和应用带来了困难。鉴于目前各种估算方法均存在一定局限,...最小临床意义差值(minimal clinically important difference,MCID)的常用估算方法包括效标法、分布法和文献分析法等。目前MCID的估算方法众多且不统一,给MCID的确定、结果解释和应用带来了困难。鉴于目前各种估算方法均存在一定局限,建议同时使用多种估算方法获得多个MCID初步估算值,并以效标法为主、其他方法为辅或将多种方法通过统计整合的估算值来确定最终的MCID。MCID可协助进行临床研究结果的临床意义判断、样本量估算以及临床决策等,在具体应用之前,应充分了解该MCID的估算方法和样本特征等相关细节以判断是否适用于所开展的研究或临床场景。展开更多
基金supported by the National Key Research and Development Program of China under grant 2016YFC0802904National Natural Science Foundation of China under grant61671470the Postdoctoral Science Foundation Funded Project of China under grant 2017M623423。
文摘Focused on the task of fast and accurate armored target detection in ground battlefield,a detection method based on multi-scale representation network(MS-RN) and shape-fixed Guided Anchor(SF-GA)scheme is proposed.Firstly,considering the large-scale variation and camouflage of armored target,a new MS-RN integrating contextual information in battlefield environment is designed.The MS-RN extracts deep features from templates with different scales and strengthens the detection ability of small targets.Armored targets of different sizes are detected on different representation features.Secondly,aiming at the accuracy and real-time detection requirements,improved shape-fixed Guided Anchor is used on feature maps of different scales to recommend regions of interests(ROIs).Different from sliding or random anchor,the SF-GA can filter out 80% of the regions while still improving the recall.A special detection dataset for armored target,named Armored Target Dataset(ARTD),is constructed,based on which the comparable experiments with state-of-art detection methods are conducted.Experimental results show that the proposed method achieves outstanding performance in detection accuracy and efficiency,especially when small armored targets are involved.
文摘最小临床意义差值(minimal clinically important difference,MCID)的常用估算方法包括效标法、分布法和文献分析法等。目前MCID的估算方法众多且不统一,给MCID的确定、结果解释和应用带来了困难。鉴于目前各种估算方法均存在一定局限,建议同时使用多种估算方法获得多个MCID初步估算值,并以效标法为主、其他方法为辅或将多种方法通过统计整合的估算值来确定最终的MCID。MCID可协助进行临床研究结果的临床意义判断、样本量估算以及临床决策等,在具体应用之前,应充分了解该MCID的估算方法和样本特征等相关细节以判断是否适用于所开展的研究或临床场景。
