IMMVP: An Efficient Daytime and Nighttime On-Road Object Detector
Journal
IEEE 21st International Workshop on Multimedia Signal Processing, MMSP 2019
Date Issued
2019
Author(s)
Abstract
It is hard to detect on-road objects under various lighting conditions. To improve the quality of the classifier, three techniques are used. We define subclasses to separate daytime and nighttime samples. Then we skip similar samples in the training set to prevent overfitting. With the help of the outside training samples, the detection accuracy is also improved. To detect objects in an edge device, Nvidia Jetson TX2 platform, we exert the lightweight model ResNet-18 FPN as the backbone feature extractor. The FPN (Feature Pyramid Network) generates good features for detecting objects over various scales. With Cascade R-CNN technique, the bounding boxes are iteratively refined for better results. ? 2019 IEEE.
Subjects
Deep learning; Embedded systems; Feature extraction; Iterative methods; Multimedia signal processing; Roads and streets; Detecting objects; Detection accuracy; Feature extractor; Feature pyramid; Lighting conditions; Object detectors; Pedestrian detection; Vehicle detection; Object detection
Type
conference paper