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It is hard to detect on-road objects under various lighting conditions. Toimprove the quality of the classifier, three techniques are used. We definesubclasses to separate daytime and nighttime samples. Then we skip similarsamples in the training set to prevent overfitting. With the help of theoutside training samples, the detection accuracy is also improved. To detectobjects in an edge device, Nvidia Jetson TX2 platform, we exert the lightweightmodel ResNet-18 FPN as the backbone feature extractor. The FPN (Feature PyramidNetwork) generates good features for detecting objects over various scales.With Cascade R-CNN technique, the bounding boxes are iteratively refined forbetter results.

IMMVP: An Efficient Daytime and Nighttime On-Road Object Detector