论文标题
Littleyolo-SPP：一种精致的实时车辆检测算法
LittleYOLO-SPP: A Delicate Real-Time Vehicle Detection Algorithm
论文作者
论文摘要
实时的车辆检测是一项具有挑战性且重要的任务。现有的实时车辆检测缺乏准确性和速度。实时系统必须在犯罪活动中检测和定位车辆，例如盗窃车辆和道路交通违规行为。在复杂场景中检测车辆的遮挡也非常困难。在这项研究中，提出了基于Yolov3微型网络的深神经网络Littleyolo-SPP的轻质模型，以实时有效地检测车辆。通过修改其功能提取网络以提高车辆检测的速度和准确性，可以改善Yolov3微小对象检测网络。拟议的网络将空间金字塔池汇总到网络中，该网络由池层的不同尺度组成，以串联功能以增强网络学习能力。边界框回归的均方根误差（MSE）和广义IOU（GIO）损耗函数用于提高网络的性能。网络培训包括来自Pascal VOC 2007,2012和MS Coco 2014数据集的基于车辆的课程，例如汽车，公共汽车和卡车。 Littleyolo-SPP网络可实时地检测车辆，无论视频框架和天气状况如何。改进的网络可在Pascal VOC上获得77.44％的较高地图，而MS COCO数据集则获得了52.95％的地图。
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.
