A deep learning model to detect foggy images for vision enhancement

Pal, Tannistha; Halder, Mritunjoy; Barua, Sattwik

doi:10.1080/13682199.2023.2185429

Cited by 1 publication

(1 citation statement)

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“…In 2023, Pal et al, [18] Convolutional neural networks (CNNs) are used in a proposed robust deep learning (RDL) model to categorize inputs as clear or foggy. Regarding training time complexity and prediction time complexity, the proposed deep neural network (DNN) architecture is effective and precise enough to categorize images as foggy or transparent.…”

Section: Literature Surveymentioning

confidence: 99%

Deep-APT: Deep Learning based Efficient Accident Prevention Technique in Fogged Environment

Lokesh

Ahilan³

2023

Preprint

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Image defogging is an innovative strategy for recovering images in foggy environments that has gotten a lot of attention in recent years because of its use in surveillance systems. The standard defogging algorithm, on the other hand, has difficulty merging the depth of picture detail and the colour of the picture. In this paper, a novel Accident Prevention Technique (Deep-APT) has been proposed to effectively restore fog-free images and prevent accidents using FasterRCNN network. Initially, a dashboard camera monitors the road ahead of the vehicle and collects video. This video sequence is converted to frames. The transformed images are pre-processed using an Adaptive dual threshold Tetrolet transform that preprocess foggy images to fog-free images it is used to remove noise in the input image. Based on the defogged image, use FasterRCNN technology to detect objects in front of the car. The Deep-APT method has been simulated using MATLAB. The experimental result shows the proposed Deep-APT yields an overall accuracy is 99.52%. As compared to existing techniques, the proposed FasterRCNN network shows better results in terms of precision, F1 score, accuracy, and recall. Using DAWN dataset, the MSE, SSIM and PSNR values for the proposed method are 0.12, 0.65 and 0.12. The Deep-APT network improves the overall accuracy of 15.43%, and 4.72% better than CR-YOLnet, and RDL respectively.

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Section: Literature Surveymentioning

confidence: 99%