Vehicle detection and distance estimation are critical components of driver assistance system and self-driving system, and considerable different frameworks have been investigated such as radar, laser and camera-based. Among them, camera-based vehicle detection and distance estimation have an obvious advantage over other systems in that it needs lower cost. However, existing camera-based methods are not robust enough under complex driving scenes. In this work, an end-to-end deep convolutional neural network framework is proposed to jointly detect vehicles and estimate vehicle distance efficiently. Specifically, a monocular depth estimation method is designed to transform the RGB appearance information into depth modality information. Then the vehicle detection module takes the RGB and depth image as inputs to improve the detection performance. Finally, the distance estimation module employs the detection results and the estimated depth information to predict the distance more precisely. The whole network can be trained in an end-to-end manner with the multi-task loss function. The proposed framework is evaluated on the public vehicle detection benchmark KITTI to show the effectiveness of the proposed framework. Moreover, the performance of three proposed sub-modules are also analysed separately to give a more comprehensive evaluation of the designed framework.
Joint vehicle detection and distance prediction via monocular depth estimation
IET Intelligent Transport Systems ; 14 , 7 ; 753-763
2020-05-06
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
driver assistance system , convolutional neural nets , computer vision , camera-based vehicle detection , detection performance , joint vehicle detection , complex driving scenes , RGB appearance information , driver information systems , monocular depth estimation method , image colour analysis , object detection , road vehicles , estimated depth information , critical components , end-to-end deep convolutional neural network framework , image sensors , depth image , vehicle detection module , depth modality information , cameras , traffic engineering computing , public vehicle detection benchmark KITTI , self-driving system , vehicle distance , distance estimation module
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