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Jiao Su, Yi An, Jialin Wu and Kai Zhang
Pedestrian detection has always been a difficult and hot spot in computer vision research. At the same time, pedestrian detection technology plays an important role in many applications, such as intelligent transportation and security monitoring. In comp...
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Haotian You, Yufang Lu and Haihua Tang
Video object detection is an important research direction of computer vision. The task of video object detection is to detect and classify moving objects in a sequence of images. Based on the static image object detector, most of the existing video objec...
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Songnan Chen, Mengxia Tang, Ruifang Dong and Jiangming Kan
The semantic segmentation of outdoor images is the cornerstone of scene understanding and plays a crucial role in the autonomous navigation of robots. Although RGB?D images can provide additional depth information for improving the performance of semanti...
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Long Li, Qi Li, Zhiyuan Liu and Lin Xue
The research results can quickly and accurately detect defects in the fabric production process.
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Wenjun Zhao, Miaolei Deng, Cong Cheng and Dexian Zhang
Object tracking is aimed at tracking a given target that is only specified in the first frame. Due to the rapid movement and the interference of cluttered backgrounds, object tracking is a significant challenging issue in computer vision. This research p...
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Soo-Jong Kim and Yong-Joo Chung
To alleviate the problem of performance degradation due to the varied sound durations of competing classes in sound event detection, we propose a method that utilizes multi-scale features for sound event detection. We employed a feature-pyramid component...
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Qian Zhang, Jie Ren, Hong Liang, Ying Yang and Lu Chen
Small object detection becomes a challenging problem in computer vision due to low resolution and less feature information. Making full use of high-resolution features is an important factor in improving small object detection. In this paper, to improve ...
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Peng Chen, Ying Li, Hui Zhou, Bingxin Liu and Peng Liu
The synthetic aperture radar (SAR) has a special ability to detect objects in any climate and weather conditions. Consequently, SAR images are widely used in maritime transportation safety and fishery law enforcement for maritime object detection. Curren...
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Yutian Wu, Shuming Tang, Shuwei Zhang and Harutoshi Ogai
Feature Pyramid Network (FPN) builds a high-level semantic feature pyramid and detects objects of different scales in corresponding pyramid levels. Usually, features within the same pyramid levels have the same weight for subsequent object detection, whi...
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Yun Ren, Changren Zhu and Shunping Xiao
The PASCAL VOC Challenge performance has been significantly boosted by the prevalently CNN-based pipelines like Faster R-CNN. However, directly applying the Faster R-CNN to the small remote sensing objects usually renders poor performance. To address thi...
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Ning Li, Tianrun Ye, Zhihua Zhou, Chunming Gao and Ping Zhang
In the domain of automatic visual inspection for miniature capacitor quality control, the task of accurately detecting defects presents a formidable challenge. This challenge stems primarily from the small size and limited sample availability of defectiv...
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Fangbin Wang, Zini Wang, Zhong Chen, Darong Zhu, Xue Gong and Wanlin Cong
To overcome the deficiencies in segmenting hot spots from thermal infrared images, such as difficulty extracting the edge features, low accuracy, and a high missed detection rate, an improved Mask R-CNN photovoltaic hot spot thermal image segmentation al...
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Gang Sun, Hancheng Yu, Xiangtao Jiang and Mingkui Feng
Edge detection is one of the fundamental computer vision tasks. Recent methods for edge detection based on a convolutional neural network (CNN) typically employ the weighted cross-entropy loss. Their predicted results being thick and needing post-process...
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Teerapong Panboonyuen, Sittinun Thongbai, Weerachai Wongweeranimit, Phisan Santitamnont, Kittiwan Suphan and Chaiyut Charoenphon
Due to the various sizes of each object, such as kilometer stones, detection is still a challenge, and it directly impacts the accuracy of these object counts. Transformers have demonstrated impressive results in various natural language processing (NLP)...
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Ignazio Gallo, Riccardo La Grassa, Nicola Landro and Mirco Boschetti
In this paper, we provide an innovative contribution in the research domain dedicated to crop mapping by exploiting the of Sentinel-2 satellite images time series, with the specific aim to extract information on ?where and when? crops are grown. The fina...
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Xungen Li, Feifei Men, Shuaishuai Lv, Xiao Jiang, Mian Pan, Qi Ma and Haibin Yu
Vehicle detection in aerial images is a challenging task. The complexity of the background information and the redundancy of the detection area are the main obstacles that limit the successful operation of vehicle detection based on anchors in very-high-...
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Longyu Tang, Tao Xie, Yunong Yang and Hong Wang
The detection of students? behaviors in classroom can provide a guideline for assessing the effectiveness of classroom teaching. This study proposes a classroom behavior detection algorithm using an improved object detection model (i.e., YOLOv5). First, ...
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Yuan Li, Mayire Ibrayim and Askar Hamdulla
In the last years, methods for detecting text in real scenes have made significant progress with an increase in neural networks. However, due to the limitation of the receptive field of the central nervous system and the simple representation of text by ...
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Zhou Fang, Xiaoyong Wang, Liang Zhang and Bo Jiang
Currently, deep learning is extensively utilized for ship target detection; however, achieving accurate and real-time detection of multi-scale targets remains a significant challenge. Considering the diverse scenes, varied scales, and complex backgrounds...
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Junyi Chen, Yanyun Shen, Yinyu Liang, Zhipan Wang and Qingling Zhang
Aircraft detection in SAR images of airports remains crucial for continuous ground observation and aviation transportation scheduling in all weather conditions, but low resolution and complex scenes pose unique challenges. Existing methods struggle with ...
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