LGD-FCOS: driver distraction detection using improved FCOS based on local and global knowledge distillation

Abstract

Ensuring safety on the road is crucial, and detecting driving distractions plays a vital role in achieving this goal. Accurate identification of distracted driving behaviors facilitates prompt intervention, thereby contributing to a reduction in accidents. We introduce an advanced fully convolutional one-stage (FCOS) object detection algorithm tailored for driving distraction detection that leverages the knowledge distillation framework. Our proposed methodology enhances the conventional FCOS algorithm through the integration of the selective kernel split-attention module. This module bolsters the performance of the backbone network, ResNet, leading to a substantial improvement in the accuracy of the FCOS target detection algorithm. In addition, we incorporate a knowledge distillation framework equipped with a novel local and global knowledge distillation loss function. This framework facilitates the student network to achieve accuracy levels comparable to that of the teacher network while maintaining a reduced parameter count. The outcomes of our approach are promising, achieving a remarkable accuracy of 92.25% with a compact model size of 31.85 million parameters. This advancement paves the way for more efficient and accurate distracted driving detection systems, ultimately contributing to enhanced road safety.