Title
POL-LWIR Vehicle Detection: Convolutional Neural Networks Meet Polarised Infrared Sensors
Abstract
For vehicle autonomy, driver assistance and situational awareness, it is necessary to operate at day and night, and in all weather conditions. In particular, long wave infrared (LWIR) sensors that receive predominantly emitted radiation have the capability to operate at night as well as during the day. In this work, we employ a polarised LWIR (POL-LWIR) camera to acquire data from a mobile vehicle, to compare and contrast four different convolutional neural network (CNN) configurations to detect other vehicles in video sequences. We evaluate two distinct and promising approaches, two-stage detection (Faster-RCNN) and one-stage detection (SSD), in four different configurations. We also employ two different image decompositions: the first based on the polarisation ellipse and the second on the Stokes parameters themselves. To evaluate our approach, the experimental trials were quantified by mean average precision (mAP) and processing time, showing a clear trade-off between the two factors. For example, the best mAP result of 80.94 % was achieved using Faster-RCNN, but at a frame rate of 6.4 fps. In contrast, MobileNet SSD achieved only 64.51 % mAP, but at 53.4 fps.
Year
DOI
Venue
2018
10.1109/CVPRW.2018.00171
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Keywords
DocType
Volume
POL-LWIR vehicle detection,vehicle autonomy,driver assistance,situational awareness,polarised LWIR,mobile vehicle,one-stage detection,polarisation ellipse,LWIR sensors,faster-RCNN,image decompositions,convolutional neural network configurations,polarised infrared sensors,Stokes parameters,mean average precision,MobileNet SSD
Conference
abs/1804.02576
ISSN
ISBN
Citations 
2160-7508
978-1-5386-6101-7
2
PageRank 
References 
Authors
0.39
16
5
Name
Order
Citations
PageRank
Marcel Sheeny121.07
Andrew Wallace220.73
Mehryar Emambakhsh3425.08
Sen Wang427921.15
Barry Connor541.14