High-Resolution Image Harmonization via Collaborative Dual Transformations

Cong, Wenyan; Xinhao, Tao,; Niu, Li; Liang, Jian‐Gang; Gao, Xuesong; Sun, Qihao; Zhang, Liqing

doi:10.48550/arxiv.2109.06671

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…The datasets collected were also mostly based on well-lit environments and did not span the winter months when temperatures are low. In the future, we will increase the time span of the data collection and enhance the data with GAN networks [31] or super-resolution techniques [32].…”

Section: Limits and Future Researchmentioning

confidence: 99%

Research on Automatic Recognition of Dairy Cow Daily Behaviors Based on Deep Learning

Yu,

Wei,

Liu

et al. 2024

Animals

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Dairy cow behavior carries important health information. Timely and accurate detection of behaviors such as drinking, feeding, lying, and standing is meaningful for monitoring individual cows and herd management. In this study, a model called Res-DenseYOLO is proposed for accurately detecting the individual behavior of dairy cows living in cowsheds. Specifically, a dense module was integrated into the backbone network of YOLOv5 to strengthen feature extraction for actual cowshed environments. A CoordAtt attention mechanism and SioU loss function were added to enhance feature learning and training convergence. Multi-scale detection heads were designed to improve small target detection. The model was trained and tested on 5516 images collected from monitoring videos of a dairy cowshed. The experimental results showed that the performance of Res-DenseYOLO proposed in this paper is better than that of Fast-RCNN, SSD, YOLOv4, YOLOv7, and other detection models in terms of precision, recall, and mAP metrics. Specifically, Res-DenseYOLO achieved 94.7% precision, 91.2% recall, and 96.3% mAP, outperforming the baseline YOLOv5 model by 0.7%, 4.2%, and 3.7%, respectively. This research developed a useful solution for real-time and accurate detection of dairy cow behaviors with video monitoring only, providing valuable behavioral data for animal welfare and production management.

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Section: Limits and Future Researchmentioning

confidence: 99%

Research on Automatic Recognition of Dairy Cow Daily Behaviors Based on Deep Learning

Yu,

Wei,

Liu

et al. 2024

Animals

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“…In the same year, Guo et al [11] proposed the Disentangled-Harmonization Transformer (D-HT) framework, which exploits the context dependence of the Transformer to ensure structural and semantic stability while enhancing the lighting and background harmony of foreground objects, thereby making the synthesized image more realistic. In addition to the above algorithms, image coordination technology based on deep learning in recent years also includes pixel-to-pixel conversion [12,13]. This method facilitates dense pixel-to-pixel conversion on low-resolution images and is increasingly used for image synthesis and coordination, heralding new trends in this field.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Harmonization Method for Fine-Grained Ship Classification

Zhang,

Zhong,

Wei

et al. 2024

Remote Sensing

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Target recognition and fine-grained ship classification in remote sensing face challenges of high inter-class similarity and sample scarcity. A transfer fusion-based ship image harmonization algorithm is proposed to overcome these challenges. This algorithm designs a feature transfer fusion strategy based on the combination of a region-aware instantiation and attention mechanism. Adversarial learning is implemented through an image harmony generator and discriminator module to generate realistic remote sensing ship harmony images. Furthermore, the domain encoder and domain discriminator modules are responsible for extracting feature representations of the foreground and background, and further align the ship foreground with remote sensing ocean background features through feature discrimination. Compared with other advanced image conversion techniques, our algorithm delivers more realistic visuals, improving classification accuracy for six ship types by 3% and twelve types by 2.94%, outperforming Sim2RealNet. Finally, a mixed dataset containing data augmentation and harmonizing samples and real data was proposed for the fine-grained classification task of remote sensing ships. Evaluation experiments were conducted on eight typical fine-grained classification algorithms, and the accuracy of the fine-grained classification for all categories of ships was analyzed. The experimental results show that the mixed dataset proposed in this paper effectively alleviates the long-tail problem in real datasets, and the proposed remote sensing ship data augmentation framework performs better than state-of-the-art data augmentation methods in fine-grained ship classification tasks.

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High-Resolution Image Harmonization via Collaborative Dual Transformations

Cited by 2 publications

References 32 publications

Research on Automatic Recognition of Dairy Cow Daily Behaviors Based on Deep Learning

Research on Automatic Recognition of Dairy Cow Daily Behaviors Based on Deep Learning

Remote Sensing Image Harmonization Method for Fine-Grained Ship Classification

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