VEHICLE DETECTION ON HIGHWAY IMAGES BASED ON SINGLE SHOT MULTIBOX DETECTOR
In this article we consider the application of the modern object detection method – Single Shot Multibox Detector. We have trained the convolutional neural network for vehicle detection on a sample of 3000 images with marked areas where are the vehicles are placed. A network quality check was performed on 7000 test images. The test and training samples contain images made by a monocular camera mounted in a vehicle moving along suburban highways during daylight hours. Recall and precision of object detection on the test sample is correspondingly more than 88% and 78%. Recognition of one frame takes 28.5 milliseconds. Experiment was performed on a graphics processor using NVidia CUDA technology. The obtained results can be applied in driver assistance systems and monitoring of the traffic situations.
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