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Xinmin Li, Yingkun Wei, Jiahui Li, Wenwen Duan, Xiaoqiang Zhang and Yi Huang
Object detection in unmanned aerial vehicle (UAV) images has become a popular research topic in recent years. However, UAV images are captured from high altitudes with a large proportion of small objects and dense object regions, posing a significant cha...
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Yiming Mo, Lei Wang, Wenqing Hong, Congzhen Chu, Peigen Li and Haiting Xia
The intrusion of foreign objects on airport runways during aircraft takeoff and landing poses a significant safety threat to air transportation. Small-scale Foreign Object Debris (FOD) cannot be ruled out on time by traditional manual inspection, and the...
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Zhiwei Lin, Weihao Chen, Lumei Su, Yuhan Chen and Tianyou Li
Object detection methods are commonly employed in power safety monitoring systems to detect violations in surveillance scenes. However, traditional object detection methods are ineffective for small objects that are similar to the background information ...
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Jian Ni, Rui Wang and Jing Tang
The detection of small objects is easily affected by background information, and a lack of context information makes detection difficult. Therefore, small object detection has become an extremely challenging task. Based on the above problems, we proposed...
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Jian Song, Zhihong Yu, Guimei Qi, Qiang Su, Jingjing Xie and Wenhang Liu
There are many small objects in UAV images, and the object scale varies greatly. When the SSD algorithm detects them, the backbone network?s feature extraction capabilities are poor; it does not fully utilize the semantic information in the deeper featur...
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Feihu Zhang, Wei Zhang, Chensheng Cheng, Xujia Hou and Chun Cao
Deep learning-based object detection methods have demonstrated remarkable effectiveness across various domains. Recently, there has been growing interest in applying these techniques to underwater environments. Conventional optical imaging methods face s...
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Linhua Zhang, Ning Xiong, Xinghao Pan, Xiaodong Yue, Peng Wu and Caiping Guo
In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algo...
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Niranjan Ravi and Mohamed El-Sharkawy
Three-dimensional object detection involves estimating the dimensions, orientations, and locations of 3D bounding boxes. Intersection of Union (IoU) loss measures the overlap between predicted 3D box and ground truth 3D bounding boxes. The localization t...
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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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Jaekyung Kim, Jungwoo Huh, Ingu Park, Junhyeong Bak, Donggeon Kim and Sanghoon Lee
Deep learning-based object detection is one of the most popular research topics. However, in cases where large-scale datasets are unavailable, the training of detection models remains challenging due to the data-driven characteristics of deep learning. S...
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Yijie Jiao, Xiaohua Wang, Wenjie Wang and Shuang Li
Deep learning has been widely used in various fields because of its accuracy and efficiency. At present, the improvement of image semantic segmentation accuracy has become the area of most concern. In terms of increasing accuracy, improved semantic segme...
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Qi Zhang, Hongying Zhang and Xiuwen Lu
In order to alleviate the situation that small objects are prone to missed detection and false detection in natural scenes, this paper proposed a small object detection algorithm for adaptive feature fusion, referred to as MMF-YOLO. First, aiming at the ...
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Changan Wei, Qiqi Li, Ji Xu, Jingli Yang and Shouda Jiang
Deep learning is widely used in vision tasks, but feature extraction of IR small targets is difficult due to the inconspicuous contours and lack of color information. This paper proposes a new convolutional neural network?based (CNN-based) method for IR ...
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Hang Yu, Jiulu Gong and Derong Chen
Detecting small objects and objects with large scale variants are always challenging for deep learning based object detection approaches. Many efforts have been made to solve these problems such as adopting more effective network structures, image featur...
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Hyeong-Ju Kang
Object detection in many real applications requires the capability of detecting small objects in a system with limited resources. Convolutional neural networks (CNNs) show high performance in object detection, but they are not adequate to resource-limite...
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Zubair Saeed, Muhammad Haroon Yousaf, Rehan Ahmed, Sergio A. Velastin and Serestina Viriri
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Lei Shi, Jiayue Sun, Yuanbo Dang, Shaoqi Zhang, Xiaoyun Sun, Lei Xi and Jian Wang
Utilizing image data for yield estimation is a key topic in modern agriculture. This paper addresses the difficulty of counting wheat spikelets using images, to improve yield estimation in wheat fields. A wheat spikelet image dataset was constructed with...
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Huan Ni, Jocelyn Chanussot, Xiaonan Niu, Hong Tang and Haiyan Guan
The large-scale variation issue in high-resolution aerial images significantly lowers the accuracy of segmenting small objects. For a deep-learning-based semantic segmentation model, the main reason is that the deeper layers generate high-level semantics...
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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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Brett Lawrence
Small unmanned aerial systems (sUAS) and relatively new photogrammetry software solutions are creating opportunities for forest managers to perform spatial analysis more efficiently and cost-effectively. This study aims to identify a method for leveragin...
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