Comparison of Image Blending Using Cycle GAN and Traditional Approach

Wyawahare, Medha; Ekbote, Ninad; Pimperkhede, Sameer; Deshpande, Atharva; Bapat, Pranav; Aphale, Ishan

doi:10.1007/978-981-19-2840-6_44

Cited by 1 publication

(1 citation statement)

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“…In our case, CycleGAN was used mainly to enhance the realism of synthetic tiles by harmonizing color and lighting conditions. While it's acknowledged that a combination of manual mixing techniques can substitute GAN for this purpose (Wyawahare et al, 2023), this avenue was not explored here. Nevertheless, synthetic tiles were generated for augmentation purposes following the use of these two GANs.…”

Section: Resultsmentioning

confidence: 99%

Enhancing Object Segmentation Model with GAN-based Augmentation using Oil Palm as a Reference

Kwong,

Kon,

Rusik

et al. 2024

Preprint

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In digital agriculture, a central challenge in automating drone applications in the plantation sector, including oil palm, is the development of a detection model that can adapt across diverse environments. This study addresses the feasibility of using GAN augmentation methods to improve palm detection models. For this purpose, drone images of young palms (< 5 year-old) from eight different estates were collected, annotated, and used to build a baseline detection model based on DETR. StyleGAN2 was trained on the extracted palms and then used to generate a series of synthetic palms, which were then inserted into tiles representing different environments. CycleGAN networks were trained for bidirectional translation between synthetic and real tiles, subsequently utilized to augment the authenticity of synthetic tiles. Both synthetic and real tiles were used to train the GAN-based detection model. The baseline model achieved precision and recall values of 95.8% and 97.2%, whereas the GAN-based model achieved precision and recall values of 98.5% and 98.6%. In the challenge dataset 1 consisting older palms (> 5 year-old), both models also achieved similar accuracies, with baseline model achieving precision and recall of 93.1% and 99.4%, and GAN-based model achieving 95.7% and 99.4%. As for the challenge dataset 2 consisting of storm affected palms, the baseline model achieved precision of 100% but recall was only 13%, whereas GAN-based model achieved a high precision and recall values of 98.7% and 95.3%. This result demonstrates that images generated by GANs have the potential to enhance the accuracies of palm detection models.

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Section: Resultsmentioning

confidence: 99%