Transformer-Based Knowledge Distillation with Ghost Attention for Multimodal Edge-Based Smart Surveillance

In the modern era, knowledge distillation has gained attention as an important technique for edge-based smart surveillance that integrates accurate yet lightweight deployable models on resource-constrained devices. However, the existing YOLOv8 based method which integrates Coordinate Attention (CA) and Masked Generative Distillation (MGD) has faced challenges, such as relying only on infrared data, losing potential features due to excessive Learnable Dilated Convolution (LDConv) usage, and the rigidity of fixed-mask distillation. This research proposes an enhanced framework that integrates cross-architecture knowledge distillation. Infrared (IR) images are collected from the Forward Looking Infrared (FLIR) dataset, and Red Green Blue (RGB) images are collected from the Korea Advanced Institute of Science and Technology (KAIST) dataset. This followed by preprocessing using letterbox resizing, mosaic augmentation, and class-balanced sampling. In the proposed cross-architecture distillation setup, a transformer-based detector is employed as the teacher to capture long-range dependencies and contextual relations across the image, whereas a lightweight YOLOv8n optimized with Ghost Attention (GA) and a hybrid convolutional design are employed as students. Finally, Adaptive Masked Generative Distillation (A-MGD), which dynamically adjusts the mask ratio and distills multilevel features, is used to enhance knowledge transfer. The experimental results demonstrated that the proposed Transformer-teacher Knowledge Distillation for YOLOv8n student (TransKD-YOLOv8n) framework achieved higher precision (74.85%) and recall (68.90%).