Fast-armored target detection based on multi-scale representation and guided anchor

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.

Path planning for light-induced dielectrophoretic manipulation of micro-particles

To realize automatic manipulation of micro-particles by light-induced dielectrophoresis （LDEP）, a path-planning scheme based on the improved artificial potential field （APF） for micro light pattern movements is proposed. An algorithm combining guided target and point obstacle based on a new local minimum judging criterion is specially designed, which can solve the local minimum problems encountered by the traditional APF. Experiments of real-time particle manipulation based on this algorithm are implemented and the experimental results show that the proposed approach can overcome the local minimum problems of the traditional APF method, and it is validated to be highly stable for intensive particle obstacles during LDEP manipulation. Consequently, this method can realize real-time manipulation of micro-nano particles with safety, decrease the difficulty of manual manipulation, and thus improve the efficiency of manipulation of micro-particles.