搜索资源列表
cnn.rar
- canny边缘检测源代码，边缘检测对于图像理解、目标检测、跟踪等问题非常重要，该代码实现了canny算子做边缘检测,source code canny edge detection, edge detection for image understanding, target detection, tracking and other issues is very important that the code to do the canny edge detection operator
cnn
- canny边缘检测源代码，边缘检测对于图像理解、目标检测、跟踪等问题非常重要，该代码实现了canny算子做边缘检测-source code canny edge detection, edge detection for image understanding, target detection, tracking and other issues is very important that the code to do the canny edge detection operator
DeepLearnToolbox_CNN_lzbV2.0
- DeepLearnToolbox_CNN_lzbV2.0 深度学习,卷积神经网络,Matlab工具箱 参考文献: [1] Notes on Convolutional Neural Networks. Jake Bouvrie. 2006 [2] Gradient-Based Learning Applied to Document Recognition. Yann LeCun. 1998 [3] https://github.com/rasmusberg
DeepLearnToolbox_CNN_lzbV3.0
- CNN - 主程序 参考文献: [1] Notes on Convolutional Neural Networks. Jake Bouvrie. 2006 [2] Gradient-Based Learning Applied to Document Recognition. Yann LeCun. 1998 [3] https://github.com/rasmusbergpalm/DeepLearnToolbox 作者：陆振波 电子
CNN
- 卷积神经网络应用的一些论文,用于目标检测和识别，像人脸，车标等-Some papers convolutional neural network applications
keras-frcnn-master
- keras实现fcnn源代码,用于视觉目标检测(fcnn source code based on keras)
slective_search
- 目标检测算法中常用到的slective search，在图像中提供候选窗口以供下一步提取特征(Slective search, which is commonly used in the target detection algorithm, provides a candidate window in the image for the next step of extracting features)
R-FCN
- R-FCN全卷积网络的目标检测神经网络，算法性能优于Faster R-CNN(R-FCN is an full convultional network, the performance of R-FCN outperform the Faster R-CNN)
labelImg-master
- LabelImg 是一个可视化的图像标定工具。使用该工具前需配置环境python + lxml。Faster R-CNN，YOLO，SSD等目标检测网络所需要的数据集，均需要借此工具标定图像中的目标。(LabelImg is a visual image calibration tool. You need to configure the environment Python + lxml before using this tool. Faster R-CNN, YOLO, SSD and
object_detect.py
- 使用CNN检测目标 基于Tensorflow目标检测API(Uses CNN to detect objects. Based on Tensorflow Object Detection API)
YOLOv4:目标检测的最优速度和精度
- 基于YOLOv4开源方案，提高卷积神经网络(CNN)的准确性。并附带开源代码地址。 https://github.com/AlexeyAB/darknet.
基于卷积神经网络的道路目标检测算法
- 针对实际交通场景下道路目标检测时存在检测精度低、检测速度慢以及难以检测小目标的问题，faster R-CNN的快速、精确道路目标检测算法。该算法包括一个精确目标区域网络 和一个目标属性学习网络通过引入反卷积结构，设计网络的损失函数，提高小目标的检测性能，为加快算法的计算速度。