The task of Human-Object Interaction （HOI） detection aims to identify all interactions between humans and objects in an image. Most existing research employs an encoder-decoder framework for end-to-end training， which relies on Absolute Positional Encoding （APE） heavily and has limited performance in complex multi-object interaction scenarios. To address the limitations of capturing relative spatial relationships between humans and objects due to reliance on APE， as well as the insufficient integration of local and global information in complex multi-object interaction scenarios， an HOI detection model was proposed by combining cross-dimensional interaction feature extraction with frequency domain feature fusion. Firstly， the conventional Transformer encoder was improved by introducing a Relative Position Encoding （RPE）， and through the fusion of RPE and APE， the model was enabled to capture the spatial relationships between humans and objects. Then， a new feature extraction module was introduced to enhance image information integration by capturing interaction features across channel， spatial， and feature dimensions， while Discrete Cosine Transform （DCT） was applied to extract frequency domain features to capture richer local and global information. Finally， the Wise-IoU loss function was adopted to improve detection accuracy and class discriminative capability， thereby allowing the model to process targets of various categories more flexibly. Experiments were conducted on two public datasets， HICO-DET and V-COCO. The results show that the proposed model achieves an improvement of 0.95 percentage points in mean Average Precision （mAP） on all categories of the HICO-DET dataset and 0.9 percentage points in AP on scenario 1 of the V-COCO dataset， compared to the GEN-VLKT （Guided Embedding Network Visual-Linguistic Knowledge Transfer） model.

Simulation engine is critical to the operation of simulation platform. Aiming at the problems of low parallelism， insufficient computing resources and difficulty in expanding of the existing Unmanned Aerial Vehicle （UAV） simulation platforms， a UAV Swarm Containerized Parallel Simulation Engine （USCPSE） with distributed framework and container mechanism was designed and implemented. In the proposed simulation engine， containers were used as the running carriers of UAV virtual entities， and the containers were deployed to multiple parallel simulation nodes to realize large-scale UAV swarm simulation. Besides， based on container live migration technology， a container scheduling strategy integrating communication and computing load was proposed， which was able to migrate containers dynamically according to communication relationships between swarms and computational load changes of simulation nodes， thereby improving comprehensive performance of large-scale UAV swarm simulation. Experimental results show that under clusters with 100， 150 and 200 nodes， compared with Message Passing Interface （MPI）-based parallel simulation architecture， USCPSE increases the speed-up ratio by 22.4%， 59.8% and 101.9%， respectively， and decreases the communication traffic by 51.8% on average.

In order to denoise image more effectively, an improved Contourlet transform denoising algorithm based on gray relational degree was proposed. On one hand, considering the gray relational degree and inter-scale from the high-frequency sub-band and low frequency sub-band by Contourlet transform, the Bayes threshold was improved; On the other hand, in order to achieve the purpose of adaptive denoising, the characteristics of Contourlet coefficients were used to improve the compromising threshold function. The experimental results show that the proposed algorithm can denoise image effectively, get higher PSNR and better visual quality, and has a good practicability.