Color–depth multi-task learning for object detection in haze

Chen, Zhe; Wang, Xin; Fan, Tanghuai; Xu, Lizhong

doi:10.1007/s00521-018-3732-6

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…The optical system's image acquisition process in this environment poses some challenges, such as contrast reduction, feature blur, and color distortion. These factors impact the quality of vision-based applications that use image features in the tunnel, including object detection [1], three-dimensional reconstruction [2], and measurement [3] tasks.…”

Section: Introductionmentioning

confidence: 99%

An Adversarial Dual-Branch Network for Nonhomogeneous Dehazing in Tunnel Construction

Shi,

Huo,

Meng

et al. 2023

Sensors

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The tunnel construction area poses significant challenges for the use of vision technology due to the presence of nonhomogeneous haze fields and low-contrast targets. However, existing dehazing algorithms display weak generalization, leading to dehazing failures, incomplete dehazing, or color distortion in this scenario. Therefore, an adversarial dual-branch convolutional neural network (ADN) is proposed in this paper to deal with the above challenges. The ADN utilizes two branches of the knowledge transfer sub-network and the multi-scale dense residual sub-network to process the hazy image and then aggregate the channels. This input is then passed through a discriminator to judge true and false, motivating the network to improve performance. Additionally, a tunnel haze field simulation dataset (Tunnel-HAZE) is established based on the characteristics of nonhomogeneous dust distribution and artificial light sources in the tunnel. Comparative experiments with existing advanced dehazing algorithms indicate an improvement in both PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity) by 4.07 dB and 0.032 dB, respectively. Furthermore, a binocular measurement experiment conducted in a simulated tunnel environment demonstrated a reduction in the relative error of measurement results by 50.5% when compared to the haze image. The results demonstrate the effectiveness and application potential of the proposed method in tunnel construction.

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Section: Introductionmentioning

confidence: 99%

An Adversarial Dual-Branch Network for Nonhomogeneous Dehazing in Tunnel Construction

Shi,

Huo,

Meng

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In [22], Chen et al propose a multi-task learning-based object detection method by jointly using the color and depth features. The method uses color and depth features to build a pair of background model, forming two streams of the proposed multi-task learning framework, and then generate the final object detection result by fusing the results given by color and depth features.…”

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confidence: 99%