LittleYOLO-SPP: A Delicate Real-Time Vehicle Detection Algorithm

TL;DR

LittleYOLO-SPP is a lightweight, real-time vehicle detection model based on YOLOv3-tiny, enhanced with spatial pyramid pooling and improved loss functions, achieving high accuracy in complex scenes.

Contribution

This paper introduces LittleYOLO-SPP, a novel real-time vehicle detection network that integrates spatial pyramid pooling into YOLOv3-tiny to improve accuracy and speed.

Findings

Achieves 77.44% mAP on PASCAL VOC

Achieves 52.95% mAP on MS COCO

Detects vehicles effectively in various weather and video conditions

Abstract

Vehicle detection in real-time is a challenging and important task. The existing real-time vehicle detection lacks accuracy and speed. Real-time systems must detect and locate vehicles during criminal activities like theft of vehicle and road traffic violations with high accuracy. Detection of vehicles in complex scenes with occlusion is also extremely difficult. In this study, a lightweight model of deep neural network LittleYOLO-SPP based on the YOLOv3-tiny network is proposed to detect vehicles effectively in real-time. The YOLOv3-tiny object detection network is improved by modifying its feature extraction network to increase the speed and accuracy of vehicle detection. The proposed network incorporated Spatial pyramid pooling into the network, which consists of different scales of pooling layers for concatenation of features to enhance network learning capability. The Mean square error (MSE) and Generalized IoU (GIoU) loss function for bounding box regression is used to increase the performance of the network. The network training includes vehicle-based classes from PASCAL VOC 2007,2012 and MS COCO 2014 datasets such as car, bus, and truck. LittleYOLO-SPP network detects the vehicle in real-time with high accuracy regardless of video frame and weather conditions. The improved network achieves a higher mAP of 77.44% on PASCAL VOC and 52.95% mAP on MS COCO datasets.