受到VMamba [31]在视觉任务领域取得的显著成果的启发,本文首次提出了mamba YOLO,一个新的SSMs模型,旨在考虑全局感知场的同时,在目标检测任务中展示其潜力。 3 Method Preliminaries 在方程(4)中,表示调整模型时间分辨率的参数,相应地,和表示给定时间间隔内连续参数的离散时间对应。这里,表示单位矩阵。变换后,模型通过...
Fast R-CNN, a top detection method [14], mistakes background patches in an image for objects because it can't see the larger context. YOLO makes less than half the number of background errors compared to Fast R-CNN. 第二,YOLO对图像进行全局推理而做出预测。与基于滑动窗口和区域建议的技术...
相关论文:DropBlock: A regularization method for convolutional networks 论文认为卷积网络对dropout并不敏感,因为池化其实就已经相当于一次dropout了,所以需要由随机drop像素变为随机drop区域 处理流程伪代码 Neck SPP RCNN系列中已经介绍过,略。 FPN + PAN 相关论文:Path aggregation network for instance segmentation....
同样,在[Performance evaluation and model quantization of object detection algorithm for infrared image]中,使用PyTorch对修改后的YOLOv5进行伪量化,并使用8位精度和静态裁剪范围。 [ Lightweight tomato real-time detection method based on improved yolo and mobile deployment]在训练后使用Nihui Convolutional Neural...
We introduce a new method of data augmentation Mosaic, and Self-Adversarial Training (SAT) We select optimal hyper-parameters while applying genetic algorithms We modify some exsiting methods to make our design suitble for efficient training and detection - modified SAM, modified PAN, and Cross mi...
photometric distortions and geometric distortions are two commonly used data augmentation method and they definitely benefit the object detection task. In dealing with photometric distortion, we adjust the brightness, contrast, hue, saturation, and noise of an image. For geometric distortion, we add ra...
但 example mining method 不适用 于一阶段的目标检测器,因为这种检测器属于密集预测架构。因此, Linet al.[45]提出了 focal loss 解决数据不平衡问题。另一个很重要的 问题是,one-hot 编码很难表达出类与类之间关联程度。这种表示方 法(one-hot)通常在打标签的时候使用。在[73]中提出的 label smoothing ...
Only two cases can be processed in this method: 1, box1 and box2 have the same shape, box1.shape == box2.shape 2, either box1 or box2 contains only one box, len(box1) == 1 or len(box2) == 1 If the shape of box1 and box2 does not match, and both of them contain ...
For those plugin modules and post-processing methods that only increase the inference cost by a small amount but can significantly improve the accuracy of object detection, we call them “bag of specials”. 主要作用是增强模型某一方面的属性,如增大感受野( receptive field),引入attention机制,增强特征整...
题目:Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions 名称:用于恶劣天气条件下目标检测的图像自适应 YOLO 论文:arxiv.org/abs/2112.0808 题目:YOLO in the Dark - Domain Adaptation Method for Merging Multiple Models 名称:用于合并多个模型的YOLO暗域自适应方法 论文:ecva.net/papers/eccv...