Abstract
Accurate localization of surrounding vehicles helps drivers to perceive surrounding environment, which can be obtained by two parameters: depth and direction angle. This research aims to present a new efficient monocular vision based pipeline to get the vehicle’s location. We proposed a plug-and-play convolutional block combination with a basic target detection algorithm to improve the accuracy of vehicle’s bounding boxes. Then they were transformed to actual depth and angle through a conversion method which was deduced by monocular imaging geometry and camera parameters. Experimental results on KITTI dataset showed the high accuracy and efficiency of the proposed method. The mAP increased by about 2% with an additional inference time of less than 5 ms. The average depth error was about 4% for near distance objects and about 7% for far distance objects. The average angle error was about two degrees.
Highlights
As of 2020, the number of global vehicles has exceeded 1.2 billion, while automobile accidents have become the eighth largest cause of injuries and death accidents worldwide
To make the localization of vehicles more rapid, accurate and targeted, we focus on the more direct methods based on target detection algorithm, which can provide the depth, angle and category of target at the same time
We presented a plug-and-play convolutional block inspired by semantic segmentation [2,3,4] to improve the accuracy of vehicle detection bounding boxes which was named Segmentation Block (SegBlock)
Summary
As of 2020, the number of global vehicles has exceeded 1.2 billion, while automobile accidents have become the eighth largest cause of injuries and death accidents worldwide. The acquisition of accurate depth and direction angle, which are used to locate surrounding vehicles helps drivers to perceive surrounding environment to prevent dangerous situations. The methods based on global depth map reconstruction are widely used. The monocular vision methods are more economical, flexible, and easy to use. With the development of deep learning, monocular vision based on convolutional neural networks has become a research hotspot. To make the localization of vehicles more rapid, accurate and targeted, we focus on the more direct methods based on target detection algorithm, which can provide the depth, angle and category of target at the same time. The accuracy of bounding boxes and the correctness of location mapping relationship directly affect the location of target, which are studied separately
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