Recent years have seen a surge in interest in object detection on remote sensing images for applications such as surveillance and management. However, challenges like small object detection, scale variation, and
在计算机视觉领域,小目标检测(SOD)一直是一个具有挑战性的任务。近年来,Transformer模型在这一领域迅速崛起,展现出了超越传统基于卷积神经网络(CNN)检测器的潜力。本文是对Aref Miri Rekavandi等人撰写的论文《Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art》的详细解读,旨在探...
Lastly, a small target detection head is appended to the existing architecture, augmenting the model鈥檚 proficiency in detecting smaller targets with heightened precision. Furthermore, various experiments are conducted on the comprehensive dataset to verify the effectiveness of UAV-YOLOv5, achieving an...
[19] Ying X, Liu L, Wang Y, et al. Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 15528-15538. [20] Aleissaee A A, Kumar A, A...
题目:What Are Expected Queries in End-to-End Object Detection? 名称:端到端对象检测中的预期查询是什么? 论文:arxiv.org/abs/2206.0123 代码:github.com/jshilong/DDQ QueryDet 题目:QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection 名称:QueryDet:用于加速高分辨率小...
Shifted window partitioning in successive blocks Swin T(Tiny),S(Small),B(Base),L(Large) • win. sz. 7x7表示使用的窗口(Windows)的大小 • dim表示feature map的channel深度) • head表示多头注意力模块中head的个数 Architecture Variants
作者的实验结果表明,在具有挑战性的场景中,作者的方法在提高目标检测精度方面是有效的。通过仔细的测试和模型融合技术,作者成功地减轻了低光环境带来的挑战,获得了满意的检测结果。展望未来,作者的方法可以进一步改进并应用于现实世界场景,以增强低光条件下的目标检测性能。 参考 [1].Low-light Object Detection....
这种端到端的思想已经在别的很多任务中大范围的使用了,但是在目标检测领域还没有人这样做(but not yet in object detection)。之前的有些类似的尝试比如soft NMS、learnable NMS,在一定程度上简化了目标检测的流程,但是要么融入了更多的先验知识,要么在benchmarks数据上取得不了很好的成绩(either add other forms ...
官方地址:https://github.com/SwinTransformer/Swin-Transformer-Object-Detection 查看源码,发现Swin Transformer并不是作为一套单独的算法进行使用,而是嵌入在mask_rcnn算法中,作为该算法的backbone。(当然,也可以使用别的算法,只是该仓库目前仅实现了mask_rcnn和cascade_mask_rcnn) 因此,有必要先对Mask R-CNN算法做...
results show that compared with the previous state-of-the-art methods on the object detection benchmark of the COCO2017 dataset, our Swin Deformable Transformer-BiPAFPN-YOLOX can significantly boost the detection precision, inference speed, and convergence speed, especially in small object detection...