A Novel Lightweight Object Detection Network with Attention Modules and Hierarchical Feature Pyramid

Abstract

Object detection methods based on deep learning typically require devices with ample computing capabilities, which limits their deployment in restricted environments such as those with embedded devices. To address this challenge, we propose Mini-YOLOv4, a lightweight real-time object detection network that achieves an excellent trade-off between speed and accuracy. Based on CSPDarknet-Tiny as the backbone network, we enhance the detection performance of the network in three ways. We use a multibranch structure embedded in an attention module for simultaneous spatial and channel attention calibration. We design a group self-attention block with a symmetric structure consisting of a pair of complementary self-attention modules to mine contextual information, thereby ensuring that the detection accuracy is improved without increasing the computational cost. Finally, we introduce a hierarchical feature pyramid network to fully exploit multiscale feature maps and promote the extraction of fine-grained features. The experimental results demonstrate that Mini-YOLOv4 requires only 4.7 M parameters and has a billion floating point operations (BFLOPs) value of 3.1. Compared with YOLOv4-Tiny, our approach achieves a 3.2% improvement in mean accuracy precision (mAP) for the PASCAL VOC dataset and obtains a significant improvement of 3.5% in overall detection accuracy for the MS COCO dataset. In testing with an embedded platform, Mini-YOLOv4 achieves a real-time detection speed of 25.6 FPS on the NVIDIA Jetson Nano, thus meeting the demand for real-time detection in computationally limited devices.

Keywords

• object detection
• embedded platform
• attention model
• feature pyramid
• real-time performance