Zobrazeno 1 - 10
of 302
pro vyhledávání: '"hybrid loss"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract The accurate detection of tunnel lining cracks and prompt identification of their primary causes are critical for maintaining tunnel availability. The advancement of deep learning, particularly in the domain of convolutional neural network (
Externí odkaz:
https://doaj.org/article/9ea7e7af78a34d5ea2ec148b544ddf70
Publikováno v:
Radioengineering, Vol 33, Iss 3, Pp 387-396 (2024)
Brain tumors refer to abnormal cell proliferation formed in brain tissue, which can cause neurological dysfunction and cognitive impairment, posing a serious threat to human health. Therefore, it becomes a very challenging work to full-automaticly se
Externí odkaz:
https://doaj.org/article/8d9353f371a84403957c4824df3fb3e7
Autor:
Mengxue Ji, Zizhe Zhou, Xinyue Wang, Weidong Tang, Yan Li, Yilin Wang, Chaoyu Zhou, Chunli Lv
Publikováno v:
Plants, Vol 13, Iss 21, p 3001 (2024)
This paper developed a radish disease detection system based on a hybrid attention mechanism, significantly enhancing the precision and real-time performance in identifying disease characteristics. By integrating spatial and channel attentions, this
Externí odkaz:
https://doaj.org/article/34ea519241c342d7914faa30a82a7281
Publikováno v:
Mathematical Biosciences and Engineering, Vol 20, Iss 11, Pp 20116-20134 (2023)
Colorectal malignancies often arise from adenomatous polyps, which typically begin as solitary, asymptomatic growths before progressing to malignancy. Colonoscopy is widely recognized as a highly efficacious clinical polyp detection method, offering
Externí odkaz:
https://doaj.org/article/9d3e97ad22c24d06aac2f285055ef551
Publikováno v:
IET Image Processing, Vol 17, Iss 8, Pp 2422-2436 (2023)
Abstract In this paper, a novel conditional focus probability learning model, termed MCNN, is proposed for multi‐focus image fusion (MFIF). Given a pair of source images, their conditional focus probabilities can be generated by using the well‐tr
Externí odkaz:
https://doaj.org/article/ef93ae10db27427fb0db2fd29ca77422
Publikováno v:
Information Processing in Agriculture, Vol 10, Iss 2, Pp 149-163 (2023)
The high similarity of shellfish images and unbalanced samples are key factors affecting the accuracy of shellfish recognition. This study proposes a new shellfish recognition method FL_Net based on a Convolutional Neural Network (CNN). We first esta
Externí odkaz:
https://doaj.org/article/3c633579cc5d4638aeaeabcfc3d3d9b6
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 3555-3567 (2023)
Deep learning methods, especially convolutional neural networks, achieve state-of-the-art performance on seismic impedance inversion. Most of the methods are based on one-dimensional (1-D) convolution, tending to yield lateral discontinuities of impe
Externí odkaz:
https://doaj.org/article/0dec3e491b10422ca693c0d4a231a479
Publikováno v:
مجله مدل سازی در مهندسی, Vol 20, Iss 71, Pp 151-163 (2022)
Detection of salient objects is done with the aim of identifying and segmenting prominent objects or areas in an image. Fully Convolutional Networks (FCNs) have shown their advantages in salient object detection; however, many previous works have foc
Externí odkaz:
https://doaj.org/article/af66b69ab7d043258f1a7489d615d0d3
Publikováno v:
Frontiers in Plant Science, Vol 14 (2023)
Identification technology of apple diseases is of great significance in improving production efficiency and quality. This paper has used apple Alternaria blotch and brown spot disease leaves as the research object and proposes a disease spot segmenta
Externí odkaz:
https://doaj.org/article/7e27a57bde014472a8bb2159b39a3be6
Publikováno v:
Journal of Imaging, Vol 10, Iss 1, p 20 (2024)
Mass segmentation is one of the fundamental tasks used when identifying breast cancer due to the comprehensive information it provides, including the location, size, and border of the masses. Despite significant improvement in the performance of the
Externí odkaz:
https://doaj.org/article/9d4fafdd53074a148d4501999ed7c37d