An improved color image defogging algorithm using dark channel model and enhancing saturation

Ma, Ruiqiang; Zhang, Shanjun

doi:10.1016/j.ijleo.2018.12.020

Cited by 17 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This technique enables to focus on details of objects which are not captured with a normal camera angle. Several image processing techniques aid to alter the colorization factor of images such as contrast [23], saturation [47], Hue [33] and YUV [72]. For this study, we have used YUV technique, which facilitates to adjust color space of images in order to highlight important regions with different color spaces.…”

Section: ) Carla Simulatormentioning

confidence: 99%

RPRP-SAP: A Robust and Precise ResNet Predictor for Steering Angle Prediction of Autonomous Vehicles

Saleem,

Asim,

Van Elst

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Steering angle controller is a core module of autonomous vehicles, where a slight miscalculation can cause severe accidents. Following safety precautions, development of robust and precise steering angle predictor is an active area of research. However, existing simulator based steering angle predictors lack in predictive performance and have not been evaluated on same benchmark datasets. Furthermore, most of them are evaluated on simulated datasets and their potential on real-world data as well as in cross-domain evaluation of both types data (real-world, simulated) remain unexplored. To accelerate and expedite research related to steering angle prediction contributions of this paper are manifold: 1) It presents two benchmark datasets that are developed using Udacity and CARLA simulators. 2) Following the need for comparative study, over both simulated datasets, it benchmarks the performance of existing predictors under 2 different evaluation settings namely: same-track and cross-track evaluation. 3) In cross-domain evaluation, it explores generalization potential of predictors by training predictors on simulated data and evaluating them on 2 realworld datasets and vice versa. 4) It presents a robust and precise steering angle predictor that utilizes skip connections for proper gradient flow among different convolutional layers. In same-track evaluation where predictors are trained and evaluated on same-track data, proposed predictor outperforms existing predictors by achieving least Mean Absolute Error (MAE) of 0.13, 0.19 and 0.065 over lake track, jungle track and CARLA based datasets, respectively. Similarly, in cross-track evaluation where predictors are trained on one track and are evaluated on other track data, once again proposed predictor outperforms existing predictors by producing average least MAE errors of 0.33 and 0.06 over Udacity and CARLA datasets, respectively. Over two real-world datasets, Sully Chen and Comma.ai, the proposed predictor demonstrates superior performance compared to existing simulator-based predictors, achieving the lowest MAE of 2.41 and 0.50, respectively.

show abstract

Section: ) Carla Simulatormentioning

confidence: 99%

RPRP-SAP: A Robust and Precise ResNet Predictor for Steering Angle Prediction of Autonomous Vehicles

Saleem,

Asim,

Van Elst

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The system then counts the number of pixels with a gray value of 0 in each row (column) of the input image. The areas with the peak number of such pixels correspond to the image edges; this quantification is conducted in different rectangular boxes [17]. The horizontal projection (along the y-axis) equation and vertical projection (along the x-axis) equation are expressed as follows:…”

Section: Character Segmentationmentioning

confidence: 99%

Automatic Receipt Recognition System Based on Artificial Intelligence Technology

2022

View full text Add to dashboard Cite

In this study, an automatic receipt recognition system (ARRS) is developed. First, a receipt is scanned for conversion into a high-resolution image. Receipt characters are automatically placed into two categories according to the receipt characteristics: printed and handwritten characters. Images of receipts with these characters are preprocessed separately. For handwritten characters, template matching and the fixed features of the receipts are used for text positioning, and projection is applied for character segmentation. Finally, a convolutional neural network is used for character recognition. For printed characters, a modified You Only Look Once (version 4) model (YOLOv4-s) executes precise text positioning and character recognition. The proposed YOLOv4-s model reduces downsampling, thereby enhancing small-object recognition. Finally, the system produces recognition results in a tax declaration format, which can upload to a tax declaration system. Experimental results revealed that the recognition accuracy of the proposed system was 80.93% for handwritten characters. Moreover, the YOLOv4-s model had a 99.39% accuracy rate for printed characters; only 33 characters were misjudged. The recognition accuracy of the YOLOv4-s model was higher than that of the traditional YOLOv4 model by 20.57%. Therefore, the proposed ARRS can considerably improve the efficiency of tax declaration, reduce labor costs, and simplify operating procedures.

show abstract

“…Once the tracking target is lost, it will not be able to complete the follow-up tracking task. Defogging is mainly achieved by image enhancement [6][7][8][9] and using a physical model [10][11][12][13][14]. Using image enhancement for image defogging does not consider the influence of fog on the image but achieves the defogging effect by adjusting the image contrast.…”

Section: Introductionmentioning

confidence: 99%

A Video-Based Real-Time Tracking Method for Multiple UAVs in Foggy Weather

Dai

Wang

2022

Electronics

View full text Add to dashboard Cite

Aiming at the real-time tracking problem of multiple unmanned aerial vehicles (UAVs) based on video under fog conditions, we propose a multitarget real-time tracking method that combines the Deepsort algorithm with detection based on improved dark channel defogging and improved You Only Look Once version 5 (YOLOv5) algorithm. The contributions of this paper are as follows: 1. For the multitarget tracking problem under fog interference, a multialgorithm combination method is proposed. 2. By optimizing dark channel defogging, the complexity of the original algorithm is reduced from \({O(n^{2})}\) to \({O(n)}\), which simplifies the processing time of the defogging algorithm. 3. The YOLOv5 network structure is optimized so that the network can synchronously reduce the detection time while maintaining high-precision detection. 4. The amount of algorithm processing through image size compression is reduced, and the real-time performance under high-precision tracking is improved. In the experiments conducted, the proposed method improved tracking precision by 36.1% and tracking speed by 39%. The average time of tracking per image frame was 0.036s, satisfying the real-time tracking of multiple UAVs in foggy weather.

show abstract

An improved color image defogging algorithm using dark channel model and enhancing saturation

Cited by 17 publications

References 2 publications

RPRP-SAP: A Robust and Precise ResNet Predictor for Steering Angle Prediction of Autonomous Vehicles

RPRP-SAP: A Robust and Precise ResNet Predictor for Steering Angle Prediction of Autonomous Vehicles

Automatic Receipt Recognition System Based on Artificial Intelligence Technology

A Video-Based Real-Time Tracking Method for Multiple UAVs in Foggy Weather

Contact Info

Product

Resources

About