Glass Refraction Distortion Object Detection via Abstract Features

Cai, Lei; Chen, Chuang; Sun, Qiankun; Chai, Haojie

doi:10.1155/2022/5456818

Cited by 3 publications

(2 citation statements)

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“…[16] can effectively improve underwater target identification performance by extracting salient features and spatial semantic information of targets and then fusing them. Cai et al [17] improved the accuracy of target detection under glass interference by minimizing the abstract feature distance between the source and target domains. Cai et al [18] proposed a framework based on transfer reinforcement learning that can improve the accuracy of cooperative multi-AUV target recognition.…”

Section: Related Workmentioning

confidence: 99%

Underwater Incomplete Target Recognition Network via Generating Feature Module

Shen

Jia

Cai

2023

International Journal of Distributed Sensor Networks

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A complex and changeable underwater archaeological environment leads to the lack of target features in the collected images, affecting the accuracy of target detection. Meanwhile, the difficulty in obtaining underwater archaeological images leads to less training data, resulting in poor generalization performance of the recognition algorithm. For these practical issues, we propose an underwater incomplete target recognition network via generating feature module (UITRNet). Specifically, for targets that lack features, features are generated by dual discriminators and generators to improve target detection accuracy. Then, multilayer features are fused to extract regions of interest. Finally, supervised contrastive learning is introduced into few-shot learning to improve the intraclass similarity and interclass distance of the target and enhance the generalization of the algorithm. The UIFI dataset is produced to verify the effectiveness of the algorithm in this paper. The experimental results show that the mean average precision (mAP) of our algorithm was improved by 0.86% and 1.29% under insufficient light and semiburied interference, respectively. The mAP for ship identification reached the highest level under all four sets of experiments.

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Section: Related Workmentioning

confidence: 99%

Underwater Incomplete Target Recognition Network via Generating Feature Module

Shen

Jia

Cai

2023

International Journal of Distributed Sensor Networks

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“…To improve the semantic performance of degraded images, X. Niu et al [22] proposed an effective image recovery framework based on generative adversarial networks. The underwater distorted target recognition network (UDTRNet) [23] and the method of binary cross-entropy loss to extract abstract features of objects [24] improved the detection accuracy of underwater targets. Object-guided dual-adversarial contrast learning [25] and multi-scale fusion algorithm [26] can effectively enhance seriously distorted underwater images.…”

Section: Related Workmentioning

confidence: 99%

Global enhancement network underwater archaeology scene parsing method

Pan,

Jia,

Cai

2023

Robotica

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Underwater archaeology is of great significance for historical and cultural transmission and preservation of underwater heritage, but it is also a challenging task. Underwater heritage is located in an environment with high sediment content, objects are mostly buried, and the water is turbid, resulting in some of the features of objects missing or blurred, making it difficult to accurately identify and understand the semantics of various objects in the scene. To tackle these issues, this paper proposes a global enhancement network (GENet) underwater scene parsing method. We introduce adaptive dilated convolution by adding an extra regression layer, which can automatically deduce adaptive dilated coefficients according to the different scene objects. In addition, considering the easy confusion in the process of fuzzy feature classification, an enhancement classification network is proposed to increase the difference between various types of probabilities by reducing the loss function. We verified the validity of the proposed model by conducting numerous experiments on the Underwater Shipwreck Scenes (USS) dataset. We achieve state-of-the-art performance compared to the current state-of-the-art algorithm under three different conditions: conventional, relic semi-buried, and turbidified water quality. The experimental results show that the proposed algorithm performs best in different situations. To verify the generalizability of the proposed algorithm, we conducted comparative experiments on the current publicly available Cityscapes, ADE20K, and the underwater dataset SUIM. The experimental results show that this paper achieves good performance on the public dataset, indicating that the proposed algorithm is generalizable.

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