ECANet: enhanced context aggregation network for single image dehazing

Cui, Zhigao; Wang, Nian; Su, Yanzhao; Zhang, Wei; Lan, Yunwei; Li, Aihua

doi:10.1007/s11760-022-02252-w

Cited by 16 publications

(6 citation statements)

References 20 publications

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“…Motivated by the works of ECA Net [ 43 ], adaptive attention is integrated into the FPN network using the Efficient Channel Attention structure which is shown in Figure 6 . Firstly, the feature layer (FL0) is extracted from the backbone network of the YOLO-ECA or fused by FPN.…”

Section: Methodsmentioning

confidence: 99%

Siam Deep Feature KCF Method and Experimental Study for Pedestrian Tracking

Tang

Jin

Liu

et al. 2023

Sensors

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The tracking of a particular pedestrian is an important issue in computer vision to guarantee societal safety. Due to the limited computing performances of unmanned aerial vehicle (UAV) systems, the Correlation Filter (CF) algorithm has been widely used to perform the task of tracking. However, it has a fixed template size and cannot effectively solve the occlusion problem. Thus, a tracking-by-detection framework was designed in the current research. A lightweight YOLOv3-based (You Only Look Once version 3) mode which had Efficient Channel Attention (ECA) was integrated into the CF algorithm to provide deep features. In addition, a lightweight Siamese CNN with Cross Stage Partial (CSP) provided the representations of features learned from massive face images, allowing the target similarity in data association to be guaranteed. As a result, a Deep Feature Kernelized Correlation Filters method coupled with Siamese-CSP(Siam-DFKCF) was established to increase the tracking robustness. From the experimental results, it can be concluded that the anti-occlusion and re-tracking performance of the proposed method was increased. The tracking accuracy Distance Precision (DP) and Overlap Precision (OP) had been increased to 0.934 and 0.909 respectively in our test data.

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Section: Methodsmentioning

confidence: 99%

Siam Deep Feature KCF Method and Experimental Study for Pedestrian Tracking

Tang

Jin

Liu

et al. 2023

Sensors

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“…Another method ECANet [15] aggregates both local and global features by a designed feature fusion block. Moreover, GFN [16] adopts the white balance (WB), contrast enhancing (CE), and gamma correction to derive three subimages from a hazy image, and directly achieve dehazing by the fusion of the subimages. Differently, GridDehazeNet [12] builds a grid dehazing network, which enhances the feature flow between different scales and depths.…”

Section: Model-free Methodsmentioning

confidence: 99%

Prior-combined dehazing network based on mutual learning

Qiao

Kong

et al. 2022

SIViP

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“…Even if the channel characteristics are compressed, the parameter quantity is still proportional to the square of the number of channels [24]. For a reduced computational burden, a one-dimensional convolution with convolution kernel length k is used to achieve local cross-channel interaction by referring to the idea of ECANet, aiming to extract the dependency between channels [25]. L-CAM represents the improved lightweight channel attention module, and the convolution kernel length k is calculated by Formula (1):…”

Section: Yolo Algorithm Improvementmentioning

confidence: 99%

An Experimental Study on Estimating the Quantity of Fish in Cages Based on Image Sonar

Zhu,

Li,

et al. 2024

JMSE

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To address the highly demanding assessment of the quantity of fish in cages, a method for estimating the fish quantity in cages based on image sonar is proposed. In this method, forward-looking image sonar is employed for continuous detection in cages, and the YOLO target detection model with attention mechanism as well as a BP neural network are combined to achieve a real-time automatic estimation of fish quantity in cages. A quantitative experiment was conducted in the South China Sea to render a database for training the YOLO model and neural network. The experimental results show that the average detection accuracy mAP50 of the improved YOLOv8 is 3.81% higher than that of the original algorithm. The accuracy of the neural network in fitting the fish quantity reaches 84.63%, which is 0.72% better than cubic polynomial fitting. In conclusion, the accurate assessment of the fish quantity in cages contributes to the scientific and intelligent management of aquaculture and the rational formulation of feeding and fishing plans.

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ECANet: enhanced context aggregation network for single image dehazing

Cited by 16 publications

References 20 publications

Siam Deep Feature KCF Method and Experimental Study for Pedestrian Tracking

Siam Deep Feature KCF Method and Experimental Study for Pedestrian Tracking

Prior-combined dehazing network based on mutual learning

An Experimental Study on Estimating the Quantity of Fish in Cages Based on Image Sonar

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