ImageNet) and applying it to object detection tasks. Pretrained models, especially those with deep convolutional neural network (CNN) architectures, capture rich hierarchical features that are beneficial for small object detection. By fine-tuning pre-trained models on target datasets, object detectors...
Object detection has been widely applied in various fields with the rapid development of deep learning in recent years. However, detecting small objects is still a challenging task because of the limited information in features and the complex background. To further enhance the detection accuracy of...
链接:https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_QueryDet_Cascaded_Sparse_Query_for_Accelerating_High-Resolution_Small_Object_Detection_CVPR_2022_paper.pd 摘要:虽然深度学习的一般目标检测在过去几年取得了很大的成功,但小目标检测的性能和效率还远远不能令人满意。促进小目标检测最常见和有效的...
我们采用 FPN 来生成 object proposals。它定义了 5 个尺度(32,64, 128,256, 512) 3 个宽高比(1; 0:5; 2) 一共 15 个anchor 来构成 object proposals. 与 GT 的 IoU≥0.7的 anchor 或者 GT 能匹配到的最大 IoU 的 anchor 作为正样本。 Small object detection by Mask R-CNN on MS COCO 在MS...
The object scale of a small object scene changes greatly, and the object is easily disturbed by a complex background. Generic object detectors do not perform well on small object detection tasks. In this paper, we focus on small object detection based on
This paper use GAN to handle the issue of small object detection which is a very hard problem in general object detection. As shown in the following figures, small object and large objects usually shown different representations from the feature level. ...
摘要原文 Abstract Small object detection is a challenging problem in computer vision. It has been widely applied in defense military, transportation, industry, etc. To facilitate in-depth understanding of small object detection, we comprehensively review the existing small object detection methods based...
The parameter count of existing models for small object detection limits their applicability on resource-constrained devices. To address these challenges, this paper introduces a detail-enhanced lightweight network (DDCNet) for small object detection. DDCNet incorporates a detail feature compensation ...
Small object detection in aerial imagery presents significant challenges in computer vision due to the minimal data inherent in small-sized objects and their propensity to be obscured by larger objects and background noise. Traditional methods using transformer-based models often face limitations stemming...
We then train a YOLOv4 model for a two-stage detection process: First, the context is recognized, then the small object of interest is detected. We evaluate our pipeline on the augmented reality device Microsoft Hololens 2. PDF Paper record ...