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Improving Small Object Detection
2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), 2017While the problem of detecting generic objects in natural scene images has been the subject of research for a long time, the problem of detection of small objects has been largely ignored. While generic object detectors perform well on medium and large sized objects, they perform poorly for the overall task of recognition of small objects.
Harish Krishna, C.V. Jawahar
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End-to-End Object Detection with Transformers
European Conference on Computer Vision, 2020We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor ...
Nicolas Carion +5 more
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Enhanced Small Object Detection Neural Network
Proceedings of the 2020 International Conference on Aviation Safety and Information Technology, 2020Faster-RCNN is an vital deep learning object detection algorithm. Nevertheless, the small object detection effect of Faster-RCNN, which does not use multi-layer feature map, is not good enough. In this paper, a new network architecture called enhanced small object detection neural network (ESOD) is proposed.
Lin Liu, Jiahao Fan, Cong Xu
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HIC-YOLOv5: Improved YOLOv5 For Small Object Detection
IEEE International Conference on Robotics and Automation, 2023Small object detection has been a challenging problem in the field of object detection. There has been some works that proposes improvements for this task, such as adding several attention blocks or changing the whole structure of feature fusion networks.
Shiyi Tang, Yini Fang, Shu Zhang
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Small Object Detection and Tracking from Aerial Imagery
2021 6th International Conference on Computer Science and Engineering (UBMK), 2021Object detection and tracking from airborne imagery draws attention to the parallel development of UAV systems and computer vision technologies. Aerial imagery has its own unique challenges that differ from the training set of modern-day object detectors, since it is made of images of larger areas compared to the regular datasets and the objects are ...
Aktaş, Mustafa, Ateş, Hasan Fehmi
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Boundary-Aware Feature Fusion With Dual-Stream Attention for Remote Sensing Small Object Detection
IEEE Transactions on Geoscience and Remote SensingDetecting small objects in remote sensing images poses significant challenges to the field of computer vision, primarily stemming from the complexity of backgrounds, limitations in pixel resolution, and information loss during the feature fusion process.
Jingnan Song +6 more
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