CNN-Enabled Visibility Enhancement Framework for Vessel Detection under Haze Environment

Lu, Yuxu; Guo, Yu; Liang, Maohan

doi:10.1155/2021/5598390

Cited by 6 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, histogram equalization produces artifacts during enhancement which is a common limitation. Similarly, the authors in [ 16 – 18 ] presented an approach which finds the optimum arrangement and strength of different image enhancement techniques through a neural network along with a new kind of layer which acquires the limitations of optimum image enhancement. However, the neural network has no specific scheme for finding the structure of neurons due to which the suitable and targeted results may not be achieved [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

Ṣiddiqi

Alsirhani

2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Most medical images are low in contrast because adequate details that may prove vital decisions are not visible to the naked eye. Also, due to the low-contrast nature of the image, it is not easily segmented because there is no significant change between the pixel values, which makes the gradient very small Hence, the contour cannot converge on the edges of the object. In this work, we have proposed an ensembled spatial method for image enhancement. In this ensembled approach, we first employed the Laplacian filter, which highlights the areas of fast intensity variation. This filter can determine the sufficient details of an image. The Laplacian filter will also improve those features having shrill disjointedness. Then, the gradient of the image has been determined, which utilizes the surrounding pixels for the weighted convolution operation for noise diminishing. However, in the gradient filter, there is one negative integer in the weighting. The intensity value of the middle pixel might be deducted from the surrounding pixels, to enlarge the difference between the head-to-head pixels for calculating the gradients. This is one of the reasons due to which the gradient filter is not entirely optimistic, which may be calculated in eight directions. Therefore, the averaging filter has been utilized, which is an effective filter for image enhancement. This approach does not rely on the values that are completely diverse from distinctive values in the surrounding due to which it recollects the details of the image. The proposed approach significantly showed the best performance on various images collected in dynamic environments.

show abstract

Section: Related Workmentioning

confidence: 99%

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

Ṣiddiqi

Alsirhani

2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…It estimates the transmission rate using haze-line prior, thereby restoring the haze-free image. Luo et al (Lu et al, 2021).…”

Section: Maritime Image Restorationmentioning

confidence: 98%

“…to enhancing the safety of maritime traffic and navigation by reducing accidents and collisions (Lu et al, 2021).…”

mentioning

confidence: 99%

Learning degradation-aware visual prompt for maritime image restoration under adverse weather conditions

He,

Jia,

2024

Front. Mar. Sci.

View full text Add to dashboard Cite

Adverse weather conditions such as rain and haze often lead to a degradation in the quality of maritime images, which is crucial for activities like navigation, fishing, and search and rescue. Therefore, it is of great interest to develop an effective algorithm to recover high-quality maritime images under adverse weather conditions. This paper proposes a prompt-based learning method with degradation perception for maritime image restoration, which contains two key components: a restoration module and a prompting module. The former is employed for image restoration, whereas the latter encodes weather-related degradation-specific information to modulate the restoration module, enhancing the recovery process for improved results. Inspired by the recent trend of prompt learning in artificial intelligence, this paper adopts soft-prompt technology to generate learnable visual prompt parameters for better perceiving the degradation-conditioned cues. Extensive experimental results on several benchmarks show that our approach achieves superior restoration performance in maritime image dehazing and deraining tasks.

show abstract

“…There are a number of different deep learning algorithms [16,17] that can be used for object detection. Algorithms like CNN (Convolution Neural Network) [18,19], RNN (Recurrent Neural Network) [20], faster R-CNN [21] are some of the most commonly used systems for training and processing. One such object detection algorithm is the YOLO (You Only Look Once) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Enhanced pothole detection system using YOLOX algorithm

K.C

2022

Auton. Intell. Syst.

View full text Add to dashboard Cite

The road is the most commonly used means of transportation and serves as a country’s arteries, so it is extremely important to keep the roads in good condition. Potholes that happen to appear in the road must be repaired to keep the road in good condition. Spotting potholes on the road is difficult, especially in a country like India where roads stretch millions of kilometres across the country. Therefore, there is a need to automate the identification of potholes with high speed and real-time precision. YOLOX is an object detection algorithm and our main goal of this article is to train and analyse the YOLOX model for pothole detection. The YOLOX model is trained with a pothole dataset and the results obtained are analysed by calculating the accuracy, recall and size of the model which is then compared to other YOLO algorithms. The experimental results in this article show that the YOLOX-Nano model predicts potholes with higher accuracy compared to other models while having low computational costs. We were able to achieve an Average Precision (AP) value of 85.6% from training the model and the total size of the model is 7.22 MB. The pothole detection capabilities of the newly developed YOLOX algorithm have never been tested before and this paper is one of the first to detect potholes using the YOLOX object detection algorithm. The research conducted in this paper will help reduce costs and increase the speed of pothole identification and will be of great help in road maintenance.

show abstract

CNN-Enabled Visibility Enhancement Framework for Vessel Detection under Haze Environment

Cited by 6 publications

References 41 publications

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

Learning degradation-aware visual prompt for maritime image restoration under adverse weather conditions

Enhanced pothole detection system using YOLOX algorithm

Contact Info

Product

Resources

About