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一个字段为空时使用另一个字段排序
查看>>
MySQL一个表A中多个字段关联了表B的ID,如何关联查询?
查看>>
MYSQL一直显示正在启动
查看>>
MySQL一站到底!华为首发MySQL进阶宝典,基础+优化+源码+架构+实战五飞
查看>>
MySQL万字总结!超详细!
查看>>
Mysql下载以及安装(新手入门,超详细)
查看>>
MySQL不会性能调优?看看这份清华架构师编写的MySQL性能优化手册吧
查看>>
MySQL不同字符集及排序规则详解:业务场景下的最佳选
查看>>
Mysql不同官方版本对比
查看>>
MySQL与Informix数据库中的同义表创建:深入解析与比较
查看>>
mysql与mem_细说 MySQL 之 MEM_ROOT
查看>>
MySQL与Oracle的数据迁移注意事项,另附转换工具链接
查看>>
mysql丢失更新问题
查看>>
MySQL两千万数据优化&迁移
查看>>
MySql中 delimiter 详解
查看>>
MYSQL中 find_in_set() 函数用法详解
查看>>
MySQL中auto_increment有什么作用?(IT枫斗者)
查看>>
MySQL中B+Tree索引原理
查看>>
mysql中cast() 和convert()的用法讲解
查看>>
mysql中datetime与timestamp类型有什么区别
查看>>