A novel MPPC-based detection method for wide range X/γ radiation dose rate measurement

Zhang, Yi; Li, D.; Tong, Tao; Huang, Xin; Wang, Y.; Liu, Y.; Zhu, Mingyang; Zhou, Weijian; Zhang, W.; Zhang, Z.; Shi, Ronghua; Wei, Lingbo

doi:10.1088/1748-0221/17/07/p07011

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To mitigate the impact of background noise on the target, the Balance Scene Learning Mechanism (BSLM; Zhang et al, 2020b) employs a generative adversarial network (GAN) to extract complex scene information from SAR. This is followed by a clustering method to differentiate between nearshore and offshore backgrounds, thus enhancing the background.…”

Section: Related Workmentioning

confidence: 99%

Context-aware SAR image ship detection and recognition network

Li,

Yue,

et al. 2024

Front. Neurorobot.

View full text Add to dashboard Cite

With the development of deep learning, synthetic aperture radar (SAR) ship detection and recognition based on deep learning have gained widespread application and advancement. However, there are still challenging issues, manifesting in two primary facets: firstly, the imaging mechanism of SAR results in significant noise interference, making it difficult to separate background noise from ship target features in complex backgrounds such as ports and urban areas; secondly, the heterogeneous scales of ship target features result in the susceptibility of smaller targets to information loss, rendering them elusive to detection. In this article, we propose a context-aware one-stage ship detection network that exhibits heightened sensitivity to scale variations and robust resistance to noise interference. Then we introduce a Local feature refinement module (LFRM), which utilizes multiple receptive fields of different sizes to extract local multi-scale information, followed by a two-branch channel-wise attention approach to obtain local cross-channel interactions. To minimize the effect of a complex background on the target, we design the global context aggregation module (GCAM) to enhance the feature representation of the target and suppress the interference of noise by acquiring long-range dependencies. Finally, we validate the effectiveness of our method on three publicly available SAR ship detection datasets, SAR-Ship-Dataset, high-resolution SAR images dataset (HRSID), and SAR ship detection dataset (SSDD). The experimental results show that our method is more competitive, with AP50s of 96.3, 93.3, and 96.2% on the three publicly available datasets, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%