To address the issues of low sensitivity to the feature parameters of new categories and difficulty in distinguishing category related and category unrelated parameters accurately of the existing few-shot object detection models， leading to unclear feature boundaries and category confusion， a Few-Shot Object Detection algorithm based on new categories Feature Enhancement and Metric Mechanism （FEMM-FSOD） was proposed. Firstly， a Cross-Domain Parameter perception Module （CDPM） was introduced to improve the neck network， thereby reconstructing re-weighting operations of channel and spatial features， and dilated convolution was combined with cross-stage information transfer and feature fusion to provide rich gradient information guidance and enhance the sensitivity of new category parameters. Meanwhile， an Integrated Correlated Multi-Feature module （ICMF） was constructed before Region of Interest Pooling （RoI Pooling） to establish correlation between features and optimize the fusion method of relevant features dynamically， thereby enhancing salient features. The introduction of CDPM and ICMF enhanced the feature representation of new categories effectively， so as to alleviate feature boundary ambiguity. Additionally， to further reduce category confusion， an orthogonal loss function based on metric mechanism， Coherence-Separation Loss （CohSep Loss）， was proposed in the detection head to achieve intra-class feature aggregation and inter-class feature separation by measuring feature vector similarity. Experimental results show that compared to the baseline algorithm TFA （Two-stage Fine-tuning Approach）， on PASCAL VOC dataset， the proposed algorithm improves the mAP50 （mean Average Precision （mAP） of new categories with threshold of 0.50） of 15 types of few-shot instance numbers by 5.3 percentage points； on COCO dataset， the proposed algorithm improves the mAP （mAP of new categories with threshold from 0.50 to 0.95） for 10shot and 30shot settings by 3.6 and 5.2 percentage points， respectively， realizing higher accuracy in few-shot object detection.

The consensus problem for a class of heterogeneous multi-Agent systems was investigated in this paper, where the multi-Agent systems were composed of first-order Agents and second-order Agents. First, consensus protocols were proposed for first-order Agents and second-order Agents, respectively. Then, necessary and sufficient condition was presented for heterogeneous multi-Agent systems with fixed and directed communication topology to reach consensus by using tools from graph theory and matrix theory. Besides, the final consensus states were specified if consensus is achieved to the case of fixed communication topology. Furthermore, sufficient condition was derived for heterogeneous multi-Agent systems with switching and directed communication topologies to reach consensus. Finally, a simulation example was given to demonstrate the effectiveness of theoretical results.