Low-Light Image Enhancement for Autonomous Driving Systems using DriveRetinex-Net

Pham, Long Hoang; Tran, Duong Nguyen-Ngoc; Jeon, Jae Wook

doi:10.1109/icce-asia49877.2020.9277442

Cited by 13 publications

(13 citation statements)

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“…An interesting approach for addressing the analysis of low-light video frames is based on the so called Retinex decomposition. [3,15]. More details in the next paragraphs.…”

Section: Related Workmentioning

confidence: 99%

“…In [3] Pham et al investigated the usage of Retinex theory as effective tool for enhancing the illumination and detail of images. They collected a Low-Light Drive (LOL-Drive) dataset and applied a deep retinex neural network, named Drive-Retinex, which was taught using this dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Although the analyzed methods allow us to obtain excellent results in enhancing video frames associated with a low-brightness driving scenario, they often fail to find an optimal trade-off between accuracy, performance, and speed of execution [3,[14][15][16][17]. The method herein proposed tries to balance the above items, providing robust and acceptable performance in a near real-time and sustainable hardware framework.…”

Section: Related Workmentioning

confidence: 99%

“…Autonomous Driving (AD) or ADAS, i.e., Advanced Driver Assisting Systems, are considered very promising technology/based solutions able to cover the safety requirements in such very complex automotive scenarios [1,2]. Both for an autonomous vehicle and assisted driving, it is critical to have a normal-light captured driving video-frames as most of the classical computer vision algorithms degrade significantly in the absence of adequate lighting [3].…”

Section: Introductionmentioning

confidence: 99%

“…The lack of sufficient illumination of the driving scene does not allow the semantic segmentation algorithms to identify and track objects or the deep classifiers to discriminate one class rather than another due to poor significant pixel-based information and therefore limited discriminating visual features [3]. Autonomous driving (AD) and driver assistance (ADAS) systems require high levels of robustness both in performance and fault-tolerance, often requiring high levels of validation and testing before being placed on the market [4].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Real-Time Deep System for Robust Objects Tracking in Low-Light Driving Scenario

Rundo

2021

Computation

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The detection of moving objects, animals, or pedestrians, as well as static objects such as road signs, is one of the fundamental tasks for assisted or self-driving vehicles. This accomplishment becomes even more difficult in low light conditions such as driving at night or inside road tunnels. Since the objects found in the driving scene represent a significant collision risk, the aim of this scientific contribution is to propose an innovative pipeline that allows real time low-light driving salient objects tracking. Using a combination of the time-transient non-linear cellular networks and deep architectures with self-attention, the proposed solution will be able to perform a real-time enhancement of the low-light driving scenario frames. The downstream deep network will learn from the frames thus improved in terms of brightness in order to identify and segment salient objects by bounding-box based approach. The proposed algorithm is ongoing to be ported over a hybrid architecture consisting of a an embedded system with SPC5x Chorus MCU integrated with an automotive-grade system based on STA1295 MCU core. The performances (accuracy of about 90% and correlation coefficient of about 0.49) obtained in the experimental validation phase confirmed the effectiveness of the proposed method.

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“…An interesting approach for addressing the analysis of low-light video frames is based on the so called Retinex decomposition. [3,15]. More details in the next paragraphs.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%