|
|
|
|
|
Jier Xi and Xiufen Ye
There are many challenges in using side-scan sonar (SSS) images to detect objects. The challenge of object detection and recognition in sonar data is greater than in optical images due to the sparsity of detectable targets. The complexity of real-world u...
ver más
|
|
|
|
|
|
|
|
|
|
|
Haiping Si, Mingchun Li, Weixia Li, Guipei Zhang, Ming Wang, Feitao Li and Yanling Li
Apples, as the fourth-largest globally produced fruit, play a crucial role in modern agriculture. However, accurately identifying apple diseases remains a significant challenge as failure in this regard leads to economic losses and poses threats to food ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xiaoqin Lian, Xue Huang, Chao Gao, Guochun Ma, Yelan Wu, Yonggang Gong, Wenyang Guan and Jin Li
In recent years, the advancement of deep learning technology has led to excellent performance in synthetic aperture radar (SAR) automatic target recognition (ATR) technology. However, due to the interference of speckle noise, the task of classifying SAR ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Minghui Sha, Dewu Wang, Fei Meng, Wenyan Wang and Yu Han
With the increasing complexity of radar jamming threats, accurate and automatic jamming recognition is essential but remains challenging. Conventional algorithms often suffer from sharply decreased recognition accuracy under low jamming-to-noise ratios (...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ziyang Wang and Irina Voiculescu
Conventional deep learning methods have shown promising results in the medical domain when trained on accurate ground truth data. Pragmatically, due to constraints like lack of time or annotator inexperience, the ground truth data obtained from clinical ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xintao Liang, Yuhang Li, Xiaomin Li, Yue Zhang and Youdong Ding
Implementing single-channel speech enhancement under unknown noise conditions is a challenging problem. Most existing time-frequency domain methods are based on the amplitude spectrogram, and these methods often ignore the phase mismatch between noisy sp...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yuzhe Bai, Fengjun Hou, Xinyuan Fan, Weifan Lin, Jinghan Lu, Junyu Zhou, Dongchen Fan and Lin Li
With the widespread application of drone technology, the demand for pest detection and identification from low-resolution and noisy images captured with drones has been steadily increasing. In this study, a lightweight pest identification model based on ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Huishan Li, Lei Shi, Siwen Fang and Fei Yin
Aiming at the problem of accurately locating and identifying multi-scale and differently shaped apple leaf diseases from a complex background in natural scenes, this study proposed an apple leaf disease detection method based on an improved YOLOv5s model...
ver más
|
|
|
|
|
|
|
|
|
|
|
Fang Ji, Junshuai Ni, Guonan Li, Liming Liu and Yuyang Wang
Underwater acoustic target recognition methods based on time-frequency analysis have shortcomings, such as missing information on target characteristics and having a large computation volume, which leads to difficulties in improving the accuracy and imme...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xulong Yu, Qiancheng Yu, Qunyue Mu, Zhiyong Hu and Jincai Xie
Traditional manual visual detection methods are inefficient, subjective, and costly, making them prone to false and missed detections. Deep-learning-based defect detection identifies the types of defects and pinpoints their locations. By employing this a...
ver más
|
|
|
|
|
|