NDLI: Vehicle detection based on improved multitask cascaded convolutional neural network and mixed image enhancement

Content Provider	IET Digital Library
Author	Xu, Ke Gong, Hua Liu, Fang
Abstract	In order to increase the detection effect of fuzzy vehicles and small size vehicles, an improved multitask cascaded convolutional neural network (IMC-CNN) based on mixed image enhancement is proposed. Firstly, contrast limited adaptive histogram equalisation and multi-scale Retinex are used to enhance images. Mixed image enhancement can effectively solve the problems of image blurring, low contrast and uneven illumination when the imaging environment is not ideal. IMC-CNN includes two stages: object location and object classification. The object location network based on multi-layer feature fusion can locate and extract the object from complex background, and output regions contain only a single vehicle object. The object classification network is a lightweight convolutional neural network with only two convolutional layers, which can effectively reduce information loss and improve the classification accuracy of small objects and fuzzy objects. In addition, online hard example mining algorithm and focal loss function are adopted in network training. These strategies can solve the problem of unbalance between positive and negative samples. To verify the validity of the proposed algorithm, the experiments are performed on SYIT-Fuzzy dataset and COCO-Vehicle dataset. Compared with Faster R-CNN, YOLO v4 and other recent models, the average classification accuracy of the proposed method is significantly increased.
Starting Page	4621
Ending Page	4632
Page Count	12
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 17, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.1005
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/17
Journal	IET Image Processing
Publisher Date	2021-01-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	COCO-Vehicle Dataset Combinatorial Mathematics Computer Vision And Image Processing Technique Convolutional Layer Data Mining Detection Effect Feature Extraction Fuzzy Objects Fuzzy Set Theory Fuzzy Vehicle Image Blurring Image Classification Image Colour Analysis Image Enhancement Image Segmentation Imaging Environment IMC-CNN Improved Multitask Learning in AI Lightweight Convolutional Neural Network Mixed Image Enhancement Multilayer Feature Fusion Network Training Neural Nets Object Classification Network Object Detection Object Location Network Optical, Image And Video Signal Processing Single Vehicle Object Size Vehicle Statistics Traffic Engineering Computing Vehicle Detection
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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