## 5 Conclusions  
This study addresses the challenges of nonlinear distortion correction and real-time stitching in high-resolution wide-angle images for visual perception tasks. A lightweight image correction and system integration framework tailored for edge computing scenarios is proposed, introducing systematic innovations in both algorithmic design and engineering deployment efficiency. At the algorithmic level, a direction-guided dynamic geometric constraint mechanism is introduced by incorporating optical flow vector directions into the TPS control point prediction path. Unlike conventional static or rule-based TPS configurations, the proposed method enables end-to-end dynamic adjustment of control points based on explicitly observed local deformation directions in the image, significantly improving the model's adaptability and correction accuracy under complex nonlinear distortions. Additionally, a multi-scale feature extraction module is designed by embedding sliding window attention mechanisms across different network levels, enabling joint modeling of multi-resolution image features. This not only enhances the network's semantic perception and fine-grained structural representation capabilities but also reduces overall computational cost through feature map partitioning and channel compression strategies. The vector direction outputs from the optical flow estimation module are further integrated into the training process as structural guidance signals, directing parameter updates toward actual deformation directions. This improves convergence efficiency and generalization ability, thereby enhancing the model's robustness to severe geometric distortions. To address the high computational complexity of Transformer-based architectures in embedded deployments, this study adopts a lightweight Swin-Tiny encoder structure. Combined with L1 regularization and attention head/channel pruning strategies, the model is compressed along both the channel redundancy and attention redundancy dimensions. This results in approximately a 40% reduction in parameter count and a 40% increase in inference speed, effectively mitigating deployment bottlenecks under resource-constrained conditions. On the deployment side, the proposed system is implemented on the Jetson TX2 NX platform using the DeepStream framework and a multithreaded optimization strategy. A dual-camera real-time stitching system is constructed, achieving a processing speed of 15 FPS at an image resolution of 1400 \(\times\) 1400. The average correction latency is 56 ms, and the stitching delay is 15 ms. The system operates stably and responds efficiently, fully validating the proposed method's feasibility and engineering applicability in embedded environments. Although this study has made notable progress in algorithm design and system implementation, there remains considerable room for further improvement. Future research may proceed along the following directions:  
Model lightweighting and cross-platform deployment: For lower-power edge devices (e.g., Jetson Nano or AI camera SoCs), advanced compression techniques such as model pruning, quantization, and knowledge distillation can be further explored to enhance deployment flexibility and adaptability across heterogeneous hardware platforms. Although the proposed model has been successfully deployed on the Jetson TX2 NX platform with favorable inference performance, most baseline algorithms are structurally complex or lack lightweight implementations, making it challenging to conduct unified benchmarking on embedded platforms. As such, this study primarily relies on inference time and model size measured in a server environment. Future work will focus on reconstructing and adapting representative baseline models for embedded deployment to establish a more comprehensive benchmarking framework.