博客
关于我
目标检测
阅读量:738 次
发布时间:2019-03-21

本文共 4012 字,大约阅读时间需要 13 分钟。

I. INTRODUCTION

Alexnet CNN architecture has become a cornerstone in modern computer vision tasks. Its success relies on several critical innovations, including data augmentation techniques and the ability to generalize from limited training data. This paper explores these aspects in depth, focusing on practical improvements for real-world applications.

II. ARCHITECTURES OF ALEXNET CNN

The Alexnet network comprises several key components: the convolutional layers, pooling operations, features extraction, and classification modules. The network's depth and regularization techniques ensure robust performance across various datasets. This section delves into the design choices that make Alexnet a reliable framework for image processing tasks.

III. PROPOSED METHOD

3.A. Data Augmentation
Data augmentation is a critical step in training deep learning models, particularly when labeled datasets are limited. Common techniques include rotation, flipping, scaling, and translation. These methods help to generate diverse training examples, improving model generalization能力提.

4.B. Training Rotation-Invariant CNN

To address rotation sensitivity, we propose a novel approach that enhances the network's invariance to rotations. By incorporating rotation augmentation during the training phase, the model learns to recognize objects regardless of their orientation in the input images.

IV. OBJECT DETECTION WITH RICNN

A. Object Proposal Detection
Proposal generation is a fundamental step in modern object detection frameworks. It selects potential regions of interest from the input image, which are then evaluated for containing objects. This process is crucial for efficient detection.

B. RICNN-Based Object Detection

R-CNN builds upon Faster R-CNN by introducing a region proposal network (RPN) to generate proposals more efficiently. This approach balances speed and accuracy, making it suitable for real-time applications. The rcnn framework has become a standard in object detection, offering robust performance across diverse scenarios.

V. EXPERIMENTS

A. Data Set Description
The experiments utilize several benchmark datasets, including PASCAL VOC and COCO. These datasets provide a comprehensive evaluation framework for testing the proposed methods. The images contain various object classes and contexts, ensuring robustness of the detection models.

B. Evaluation Metrics

We employ standard metrics for object detection, such as accuracy, recall, precision, and F1-score. These metrics assess both the ability of the model to detect objects and its accuracy in localization. The evaluation process ensures fair comparison across different approaches.

C. Implementation Details and Parameter Optimization

The implementation leverages state-of-the-art tools and frameworks. We use Python with PyTorch for prototyping and TensorFlow for production-ready models. Parameter optimization is performed using techniques like grid search and Bayesian methods to maximize model performance.

D. SVMs Versus Softmax Classifier

This study compares support vector machines (SVMs) and softmax classifiers in the context of object detection. While SVMs excel at linear classification tasks, softmax functions are more suitable for deep learning models due to their ability to handle non-linear decision boundaries.

E. Experimental Results and Comparisons

The experimental results demonstrate the effectiveness of the proposed methods in various scenarios. We compare our approach with existing baselines and highlight improvements in accuracy and efficiency. The experiments also show that the proposed rotation-invariant CNN significantly outperforms traditional methods in rotation-sensitive tasks.

参考文献

[1] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems. 2012.
[2] He K, Zhang X, Ren S, et al. Deep residual learning //Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

转载地址:http://yiggz.baihongyu.com/

你可能感兴趣的文章
MySQL 常见的开放性问题
查看>>
Mysql 常见错误
查看>>
mysql 常见问题
查看>>
MYSQL 幻读(Phantom Problem)不可重复读
查看>>
mysql 往字段后面加字符串
查看>>
mysql 快照读 幻读_innodb当前读 与 快照读 and rr级别是否真正避免了幻读
查看>>
MySQL 快速创建千万级测试数据
查看>>
mysql 快速自增假数据, 新增假数据,mysql自增假数据
查看>>
MySql 手动执行主从备份
查看>>
Mysql 批量修改四种方式效率对比(一)
查看>>
mysql 批量插入
查看>>
Mysql 报错 Field 'id' doesn't have a default value
查看>>
MySQL 报错:Duplicate entry 'xxx' for key 'UNIQ_XXXX'
查看>>
Mysql 拼接多个字段作为查询条件查询方法
查看>>
mysql 排序id_mysql如何按特定id排序
查看>>
Mysql 提示:Communication link failure
查看>>
mysql 插入是否成功_PDO mysql:如何知道插入是否成功
查看>>
Mysql 数据库InnoDB存储引擎中主要组件的刷新清理条件:脏页、RedoLog重做日志、Insert Buffer或ChangeBuffer、Undo Log
查看>>
mysql 数据库中 count(*),count(1),count(列名)区别和效率问题
查看>>
mysql 数据库备份及ibdata1的瘦身
查看>>