FH-DETR A faster helmet detection transformer for small, complex, confusion two-wheeler helmet

Abstract The field of helmet detection faces challenges due to small targets, complex backgrounds, and confusion with neighboring objects. This paper proposes a faster real-time two-wheeler helmet detection model based on the Real-Time Detection Transformer (RT-DETR) to address these issues. Faster Helmet-DETR (FH-DETR) includes a new FasterRepConvRBlock (FRBlock) structure designed using model re-parameterization techniques to improve detection performance while meeting real-time requirements for practical applications. Additionally, the proposal introduces a Mixed local channel attention (MLCA) module to address the issue of object confusion with neighboring objects. The module combines channel and spatial information, as well as local and global information, resulting in a significant improvement in network performance.A new module called the Cross-Stage Partial Parallel Atrous Convolution (CSPPAC) is proposed to increase the receptive field. This is achieved by using convolutions with different atrous rates to capture multi-scale information and enhance feature representation. Furthermore, the detection performance for small-sized helmet objects is improved through the use of a new channel-gated up-sampling and down-sampling technique to strengthen meaningful features and suppress redundant and irrelevant features. The experimental results indicate that FH-DETR enhances the mAP50 by 2.3\% and increases the FPS to 141.3 on the helmet dataset. These improvements significantly enhance the model's capability in detecting small objects and dense scenes, meeting the real-time requirement while ensuring detection accuracy. FH-DETR provides an effective solution for the real-time detection of two-wheeler helmets.