An Efficient Object Detection Model Based on NanoDet
Van-Thanh Hoang, Tu Minh Phuong, Ha-Phuong Nguyen, Vu Huu Dao, Kang-Hyun Jo · 2025
Object detection is a fundamental task in computer vision, involving the prediction of bounding boxes and class labels for Regions of Interest (ROI) within images. Traditionally, anchor-based detectors have dominated this field, leveraging predefined anchor boxes for object localization and classification. However, anchor-free detectors have emerged as a compelling alternative due to their reduced computational complexity and efficient detection capabilities. Among these innovations, Nan-oDet has garnered significant attention in the AI community for its speed and lightweight design as an anchor-free object detection model. This study introduces an enhanced variant of NanoDet, leveraging the EfficientNetB0-compact model as its backbone. By replacing the original ShuffleNetV2 backbone, this integration aims to improve detection performance without compromising speed, thereby enhancing suitability for mobile devices. Experimental results demonstrate the effectiveness of this modification in achieving superior performance metrics, highlighting the potential of advanced network architectures in optimizing object detection tasks for mobile and edge computing environments.