Real-time object detection method based on improved YOLOv4-tiny

TL;DR

This paper proposes an improved YOLOv4-tiny-based object detection method that enhances speed and maintains accuracy, making it more suitable for real-time applications on mobile and embedded devices.

Contribution

It introduces a novel auxiliary residual network with attention mechanisms and replaces certain modules to reduce computation while improving feature extraction.

Findings

Faster detection speed than YOLOv4-tiny and YOLOv3-tiny.

Maintains similar average precision to YOLOv4-tiny.

Suitable for real-time object detection on resource-constrained devices.

Abstract

The "You only look once v4"(YOLOv4) is one type of object detection methods in deep learning. YOLOv4-tiny is proposed based on YOLOv4 to simple the network structure and reduce parameters, which makes it be suitable for developing on the mobile and embedded devices. To improve the real-time of object detection, a fast object detection method is proposed based on YOLOv4-tiny. It firstly uses two ResBlock-D modules in ResNet-D network instead of two CSPBlock modules in Yolov4-tiny, which reduces the computation complexity. Secondly, it designs an auxiliary residual network block to extract more feature information of object to reduce detection error. In the design of auxiliary network, two consecutive 3x3 convolutions are used to obtain 5x5 receptive fields to extract global features, and channel attention and spatial attention are also used to extract more effective information. In the end, it merges the auxiliary network and backbone network to construct the whole network structure of improved YOLOv4-tiny. Simulation results show that the proposed method has faster object detection than YOLOv4-tiny and YOLOv3-tiny, and almost the same mean value of average precision as the YOLOv4-tiny. It is more suitable for real-time object detection.