基于SSD+Resnet的车牌检测识别毕业设计资源-CSDN下载

共1170个文件

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cpp：220个

hpp：128个

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毕业设计

Resnet

车牌检测识别

94 浏览量 2023-07-01 21:32:40 上传评论 1 收藏 120.85MB ZIP 举报

标题中的“基于SSD+Resnet的车牌检测识别”是一个深度学习领域的项目，涉及计算机视觉技术，特别是目标检测和字符识别。SSD（Single Shot MultiBox Detector）是一种高效的实时目标检测算法，它通过在一个单一的神经网络中同时预测物体边界框和类别来实现快速检测。而Resnet（Residual Network）是深度学习网络架构，因其残差块设计解决了深度网络中的梯度消失和爆炸问题，使得训练非常深的模型成为可能。在描述中，“毕业设计”意味着这是一个学生为完成学业而进行的项目，通常包括研究、开发和报告撰写等环节，旨在展示学生对某一特定领域的理解和应用能力。结合标签，我们可以推测这个毕业设计的主要内容是构建一个车牌检测与识别系统，该系统利用SSD进行车辆车牌的定位，然后借助Resnet处理提取出的车牌图像，进行字符识别。标签“Resnet”强调了在模型中使用了残差网络，Resnet因其在ImageNet竞赛中的优异表现而被广泛应用。它的核心思想是引入“跳跃连接”，使得信息可以直接从前一层传递到后一层，有效解决了深度网络训练的问题。 “车牌检测识别”是该项目的核心任务。车牌检测是找出图像中所有车牌的过程，一般涉及图像预处理、特征提取和边界框预测。而车牌识别则是在检测到的车牌图像上，进一步识别出各个字符，这通常涉及到字符分割、特征提取和分类。在提供的压缩文件名“Caffe_SSD_LSTM_LPR-master”中，我们可以推测出项目可能使用了Caffe框架来实现SSD模型。Caffe是一个流行且高效的深度学习框架，以其速度和简洁性著称。LSTM（Long Short-Term Memory）是循环神经网络的一种，常用于序列数据的处理，这里可能是用来处理连续的字符序列，帮助提高车牌字符识别的准确性。LPR代表“License Plate Recognition”，即车牌识别，表明这个项目不仅包括车牌检测，还有完整的字符识别流程。这个毕业设计项目综合运用了深度学习的多种技术，包括SSD目标检测、Resnet特征提取、Caffe框架以及LSTM的序列建模，旨在实现高效准确的车牌检测与识别系统。在实际应用中，这样的系统可以应用于智能交通、停车场管理等领域，具有较高的实用价值。

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基于SSD+Resnet的车牌检测识别毕业设计（1170个子文件）

lpr_detection.caffemodel 90.6MB

lpr_recognition.caffemodel 12.16MB

lpr_resnet_ctc.caffemodel 6.07MB

caffe.pb.cc 1.66MB

gtest_main.cc 2KB

ccpd_train_lmdb 55B

ccpd_val_lmdb 53B

caffe.cloc 1KB

Utils.cmake 13KB

Cuda.cmake 11KB

Summary.cmake 7KB

Targets.cmake 7KB

FindLAPACK.cmake 7KB

Dependencies.cmake 6KB

ConfigGen.cmake 4KB

ProtoBuf.cmake 4KB

FindMKL.cmake 3KB

FindNumPy.cmake 2KB

gflags.cmake 2KB

Misc.cmake 2KB

FindMatlabMex.cmake 2KB

FindLevelDB.cmake 2KB

glog.cmake 2KB

FindAtlas.cmake 2KB

FindOpenBLAS.cmake 2KB

FindGFlags.cmake 2KB

lint.cmake 1KB

FindGlog.cmake 1KB

FindvecLib.cmake 1KB

FindLMDB.cmake 1KB

FindSnappy.cmake 1KB

FindNCCL.cmake 654B

CNAME 25B

gtest-all.cpp 329KB

bbox_util.cpp 86KB

test_net.cpp 79KB

test_upgrade_proto.cpp 71KB

test_bbox_util.cpp 70KB

test_pooling_layer.cpp 50KB

test_gradient_based_solver.cpp 44KB

test_convolution_layer.cpp 43KB

upgrade_proto.cpp 41KB

net.cpp 39KB

data_transformer.cpp 39KB

test_neuron_layer.cpp 34KB

test_annotated_data_layer.cpp 26KB

io.cpp 25KB

test_split_layer.cpp 25KB

im_transforms.cpp 23KB

solver.cpp 23KB

test_prior_box_layer.cpp 22KB

test_scale_layer.cpp 21KB

caffe_.cpp 21KB

test_bias_layer.cpp 19KB

detection_output_layer.cpp 19KB

test_multibox_loss_layer.cpp 17KB

test_random_number_generator.cpp 17KB

window_data_layer.cpp 17KB

test_lrn_layer.cpp 17KB

_caffe.cpp 16KB

test_data_transformer.cpp 16KB

test_io.cpp 16KB

base_conv_layer.cpp 16KB

test_inner_product_layer.cpp 15KB

multibox_loss_layer.cpp 15KB

test_data_layer.cpp 15KB

caffe.cpp 15KB

blob.cpp 14KB

sgd_solver.cpp 14KB

interp.cpp 14KB

test_detection_output_layer.cpp 13KB

parallel.cpp 13KB

annotated_data_layer.cpp 12KB

test_deconvolution_layer.cpp 12KB

test_accuracy_layer.cpp 11KB

pooling_layer.cpp 11KB

recurrent_layer.cpp 11KB

test_memory_data_layer.cpp 11KB

lrn_layer.cpp 11KB

ctc_decoder_layer.cpp 11KB

test_lstm_layer.cpp 10KB

common.cpp 10KB

test_normalize_layer.cpp 10KB

test_crop_layer.cpp 10KB

test_argmax_layer.cpp 10KB

math_functions.cpp 10KB

cudnn_conv_layer.cpp 10KB

batch_norm_layer.cpp 10KB

test_reshape_layer.cpp 10KB

normalize_layer.cpp 10KB

test_blob.cpp 9KB

test_reduction_layer.cpp 9KB

im2col.cpp 9KB

scale_layer.cpp 9KB

layer_factory.cpp 9KB

detection_evaluate_layer.cpp 9KB

lstm_layer.cpp 9KB

warp_ctc_loss_layer.cpp 8KB

rnn_layer.cpp 8KB

im2col_layer.cpp 8KB

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