YOLO-RACE: reassembly and convolutional block attention for enhanced dense object detection

Object detection automatically identifies and locates specific objects within images or videos, and plays a critical role in various fields. Among the many approaches, the YOLO model has significantly improved the accuracy and speed of object detection, thereby garnering attention for real-time processing. However, it exhibits limitations in detecting small or densely packed objects. This study introduces YOLO-RACE (You Only Look Once with ReAssembly and Convolutional block attEntion), an improved model based on YOLOv8 by incorporating the CARAFE upsampling technique, ResBlock, and the Convolutional Block Attention Module. YOLOv8 was selected from among various YOLO models because of its ability to deliver high accuracy in complex object detection tasks while maintaining its lightweight architecture and overall efficiency. The proposed model was evaluated using the VisDrone 2019, Pascal VOC, and SKU-110 K datasets. The experimental results demonstrated a precision of 0.419, recall of 0.321, mAP50 of 0.316, mAP50@95 of 0.18, and F1-score of 0.364 on the VisDrone 2019 dataset. On the Pascal VOC dataset, the model achieved a precision of 0.764, recall of 0.699, mAP50 of 0.773, mAP50@95 of 0.559, and F1-score of 0.73. Furthermore, on the SKU-110 K dataset, the model attained a precision of 0.904, recall of 0.841, mAP50 of 0.902, mAP50@95 of 0.575, and F1-score of 0.871. Based on these comparative results, the proposed model achieved superior performance compared with YOLOv8 and its variants, as well as with other state-of-the-art models such as YOLOv9, YOLOv10, YOLOv11, GELAN, and EfficientDet. This code is available at https://github.com/AnBLab-BAE/YOLO-RACE.git .