Estimation of distance to objects by stereovision

Muratov, Yevgeniy R.; Nikiforov, Michael B.; Rusakov, Alexey B.; Gurov, Victor S.

doi:10.1109/meco.2015.7181890

Cited by 6 publications

(1 citation statement)

References 5 publications

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“…In addition, in outdoor conditions or under strong sunlight, the camera cannot differentiate the LED light and sunlight, making distance detection impossible. To mitigate these issues, some proposed a depth estimation method using a stereo vision sensor, which requires a large calculation to process data and is prone to external noise, such as dirt or lighting [ 27 , 28 , 29 ]. Godard et al [ 30 ] proposed Monodepth, a model that trains CNN using single images and a disparity map, which shows the difference between two images and extracts depth information from the image reconstruction based on single images.…”

Section: Image Dehazing With Depth Estimationmentioning

confidence: 99%

Deep Multimodal Detection in Reduced Visibility Using Thermal Depth Estimation for Autonomous Driving

Yoon

Cho

2022

Sensors

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Recently, the rapid development of convolutional neural networks (CNN) has consistently improved object detection performance using CNN and has naturally been implemented in autonomous driving due to its operational potential in real-time. Detecting moving targets to realize autonomous driving is an essential task for the safety of drivers and pedestrians, and CNN-based moving target detectors have shown stable performance in fair weather. However, there is a considerable drop in detection performance during poor weather conditions like hazy or foggy situations due to particles in the atmosphere. To ensure stable moving object detection, an image restoration process with haze removal must be accompanied. Therefore, this paper proposes an image dehazing network that estimates the current weather conditions and removes haze using the haze level to improve the detection performance under poor weather conditions due to haze and low visibility. Combined with the thermal image, the restored image is assigned to the two You Only Look Once (YOLO) object detectors, respectively, which detect moving targets independently and improve object detection performance using late fusion. The proposed model showed improved dehazing performance compared with the existing image dehazing models and has proved that images taken under foggy conditions, the poorest weather for autonomous driving, can be restored to normal images. Through the fusion of the RGB image restored by the proposed image dehazing network with thermal images, the proposed model improved the detection accuracy by up to 22% or above in a dense haze environment like fog compared with models using existing image dehazing techniques.

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