登录
首页 » Python » 完成版LaneNet

完成版LaneNet

于 2020-10-28 发布
0 242
下载积分: 1 下载次数: 2

代码说明:

说明:  基于SegNet实现了车道线的识别。里面包含已经训练好的模型。(Lane line recognition based on SegNet contains the trained model.)

文件列表:

data, 0 , 2018-12-30
data\source_image, 0 , 2018-12-30
data\source_image\accuracy.png, 48361 , 2018-12-13
data\source_image\binary_seg_loss.png, 47406 , 2018-12-13
data\source_image\instance_seg_loss.png, 45704 , 2018-12-13
data\source_image\lanenet_batch_test.gif, 40673826 , 2018-12-13
data\source_image\lanenet_binary_seg.png, 51954 , 2018-12-13
data\source_image\lanenet_embedding.png, 643503 , 2018-12-13
data\source_image\lanenet_instance_seg.png, 37788 , 2018-12-13
data\source_image\lanenet_mask_result.png, 1007811 , 2018-12-13
data\source_image\network_architecture.png, 178176 , 2018-12-13
data\source_image\total_loss.png, 43865 , 2018-12-13
data\training_data_example, 0 , 2018-12-30
data\training_data_example\gt_image_binary, 0 , 2018-12-30
data\training_data_example\gt_image_binary\0000.png, 6807 , 2018-12-13
data\training_data_example\gt_image_binary\0001.png, 6849 , 2018-12-13
data\training_data_example\gt_image_binary\0002.png, 7700 , 2018-12-13
data\training_data_example\gt_image_binary\0003.png, 7293 , 2018-12-13
data\training_data_example\gt_image_binary\0004.png, 6584 , 2018-12-13
data\training_data_example\gt_image_binary\0005.png, 6632 , 2018-12-13
data\training_data_example\gt_image_instance, 0 , 2018-12-30
data\training_data_example\gt_image_instance\0000.png, 7598 , 2018-12-13
data\training_data_example\gt_image_instance\0001.png, 7652 , 2018-12-13
data\training_data_example\gt_image_instance\0002.png, 8654 , 2018-12-13
data\training_data_example\gt_image_instance\0003.png, 8226 , 2018-12-13
data\training_data_example\gt_image_instance\0004.png, 7313 , 2018-12-13
data\training_data_example\gt_image_instance\0005.png, 7370 , 2018-12-13
data\training_data_example\image, 0 , 2018-12-30
data\training_data_example\image\0000.png, 1113990 , 2018-12-13
data\training_data_example\image\0001.png, 1135520 , 2018-12-13
data\training_data_example\image\0002.png, 1210780 , 2018-12-13
data\training_data_example\image\0003.png, 1192757 , 2018-12-13
data\training_data_example\image\0004.png, 1166130 , 2018-12-13
data\training_data_example\image\0005.png, 1085884 , 2018-12-13
data\training_data_example\train.txt, 988 , 2018-12-13
data\training_data_example\val.txt, 493 , 2018-12-13
data\tusimple_test_image, 0 , 2018-12-30
data\tusimple_test_image\0.jpg, 183035 , 2018-12-13
data\tusimple_test_image\1.jpg, 213446 , 2018-12-13
data\tusimple_test_image\2.jpg, 189109 , 2018-12-13
data\tusimple_test_image\3.jpg, 221499 , 2018-12-13
data\tusimple_test_image\4.jpg, 211132 , 2018-12-13
data\tusimple_test_image\ret, 0 , 2018-12-30
data\tusimple_test_image\ret\0.jpg, 204076 , 2018-12-29
data\tusimple_test_image\ret\1.jpg, 226300 , 2018-12-29
data\tusimple_test_image\ret\2.jpg, 205588 , 2018-12-29
data\tusimple_test_image\ret\3.jpg, 234343 , 2018-12-29
data\tusimple_test_image\ret\4.jpg, 222604 , 2018-12-29
tools, 0 , 2019-03-30
tools\__pycache__, 0 , 2018-12-30
tools\__pycache__\cnn_basenet.cpython-35.pyc, 14265 , 2018-12-29
tools\__pycache__\dense_encoder.cpython-35.pyc, 6066 , 2018-12-29
tools\__pycache__\fcn_decoder.cpython-35.pyc, 2872 , 2018-12-29
tools\__pycache__\global_config.cpython-35.pyc, 879 , 2018-12-29
tools\__pycache__\lanenet_cluster.cpython-35.pyc, 6235 , 2018-12-29
tools\__pycache__\lanenet_discriminative_loss.cpython-35.pyc, 3924 , 2018-12-29
tools\__pycache__\lanenet_merge_model.cpython-35.pyc, 4976 , 2018-12-29
tools\__pycache__\lanenet_postprocess.cpython-35.pyc, 2620 , 2018-12-29
tools\__pycache__\vgg_encoder.cpython-35.pyc, 4484 , 2018-12-29
tools\cnn_basenet.py, 16846 , 2018-12-13
tools\dense_encoder.py, 7947 , 2018-12-29
tools\fcn_decoder.py, 3425 , 2018-12-29
tools\generate_tusimple_dataset.py, 6337 , 2018-12-13
tools\global_config.py, 1643 , 2018-12-13
tools\lanenet_cluster.py, 6823 , 2018-12-13
tools\lanenet_discriminative_loss.py, 5494 , 2018-12-13
tools\lanenet_merge_model.py, 7253 , 2018-12-29
tools\lanenet_postprocess.py, 2565 , 2018-12-13
tools\test_lanenet.py, 9905 , 2019-03-30
tools\train_lanenet.py, 14860 , 2018-12-13
tools\vgg_encoder.py, 6720 , 2018-12-29

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • 相机标定源代码
    相机标定,Tsai 张正友算法,Matlab实现((Camera calibration, Tsai zhangzhengyou algorithm, Matlab achieve))
    2018-05-01 13:35:32下载
    积分:1
  • dajinfaanddiedaifa
    基于大津法和迭代法的图像二值化程序,相当于利用该方法进行了图像的分割,源码已调试,很好使!(Based on the Otsu method and iterative method of image binarization procedure is equivalent to using the method of image segmentation, source code has been debugging, very good to make!)
    2009-09-17 17:58:47下载
    积分:1
  • vtk-point-cloud-rendering
    vtk显示点云,和点云三角网格化。 源代码已可运行,不过vtk的环境需要你自己配置(vtk display point clouds, and point cloud triangular grid. Source code has been run, but vtk environment requires you to configure)
    2021-01-26 23:18:41下载
    积分:1
  • GEE平台LULC分类
    说明:  基于Landsat8卫星影像数据,在GEE平台中的监督分类(Supervised classification based on landsat8 satellite image data in GEE platform)
    2020-12-27 19:04:14下载
    积分:1
  • bitmap
    说明:  实现了打开一副位图,显示起像素分布的直方图(Figure shows the histogram of open spaces)
    2011-03-26 08:28:33下载
    积分:1
  • faxiangliang
    点云三维重建中需要用的法向量计算,能够有效避免法向量不一致的问题,更好进行三维重建。测试有用。(Normal vector calculation point cloud reconstruction in need, and can effectively avoid the problem of inconsistent normals, better three-dimensional reconstruction. Useful for testing.)
    2020-10-08 14:37:36下载
    积分:1
  • 像纹理
    实图像的纹理特征提取,提取出的效果比较好,希望能够绑到有用的同学和人员(Real image texture feature extraction, the effect is better, hoping to be tied to useful classmates and personnel.)
    2018-04-14 20:15:04下载
    积分:1
  • hyit_DW
    VC++实现数字水印技术,在BMP位图中添加各种水印信息,程序分LSB和DWT嵌入方法。(VC++ implementation of digital watermarking technology, adding various watermarks in BMP bitmap, the program points LSB and DWT embedding methods.)
    2014-11-19 13:29:28下载
    积分:1
  • DensePose-master
    说明:  DensePose用深度学习把2D图像坐标映射到3D人体表面上,再加上以每秒多帧的速度处理密集坐标,最后实现动态人物的精确定位和姿态估计。该技术集目标检测、姿态估计、目标部分/实例分割等多种计算机视觉任务于一身的一个综合问题。(DensePost maps 2D image coordinates to 3D human body surface by in-depth learning, and processes dense coordinates at the speed of multiple frames per second. Finally, it realizes precise positioning and attitude estimation of dynamic characters. This technology integrates many kinds of computer vision tasks, such as target detection, attitude estimation, target part/instance segmentation, etc.)
    2019-06-17 21:25:31下载
    积分:1
  • asmsnake
    说明:  snakes模型的代码(matlab语言),包括GVF模型代码,里面含使用说明。(snakes model code (matlab language), including the GVF model code, which contains instructions.)
    2010-03-18 17:48:51下载
    积分:1
  • 696518资源总数
  • 106242会员总数
  • 10今日下载