Target Detection Based On Feature Fusion Of Multi-Scale Branch Structure

In the field of computer vision,target detection is one of the most basic tasks,and plays an important role in image analysis and understanding.Based on the convolutional neural network,the paper uses deep learning to extract the target features.Finally,the task of locating and classifying specific targets in the image is completed,which provides support for the computer to understand the scene and situational awareness of the image.The paper first analyzes and summarizes the traditional classic target detection algorithms and compares them with the deep learning method based on CNN network.The analysis finds that the classical target detection algorithm has good effects in a single target and simple scene,but it is often difficult to achieve target detection for multiple types of targets and complex scenes.The convolutional network is similar to the process of human cognitive goals,and the semantic features of the target are extracted in a learning way.The learned network features are general and universal,and have great advantages for the recognition of multiple types of targets in complex scenarios.There are still many problems with using convolutional neural networks for target detection.First,the deepening of the number of network layers is helpful for the extraction of higher-level semantic features,but the deeper convolution network makes the location information of the target more and more blurred,resulting in a decrease in positioning accuracy.Secondly,deep convolutional networks and fully connected networks require a huge amount of computation.How to improve the detection speed while maintaining the detection accuracy is also crucial for the performance of the algorithm.In view of the above problems,the paper studies the target detection method based on deep learning.From the target location method and classification method,the advantages and disadvantages of various schemes are analyzed.Finally,the end-to-end detection method that directly returns the target coordinates and categories from the convolutional layer is used as the basic form of the detection network.The end-to-end detection network has the characteristics of high computing speed,but there are problems such as insufficient feature extraction and unbalanced positive and negative samples.This article has conducted the following research on these issues.Firstly,aiming at the problem of insufficient feature extraction,this paper uses a feature fusion of multi-scale branch structure to realize a high-speed feature extraction module,and realizes the feature fusion for multi-scale receptive field through connection in the module.The feature representation of the network is enhanced by introducing a rectangular convolution kernel.By adjusting the structure of the convolutional network,the multi-layer network features are re-converged.The multi-scale receptive field,rectangular convolution kernel and multi-layer feature fusion method effectively improve the problem of insufficient network feature extraction,and use parallel convolution structure in the feature fusion to improve the detection accuracy while ensuring real time of the algorithm.Aiming at the problem of unbalanced positive and negative samples,the paper considers the basic principles of convolutional networks,and analyzes and verifies them from three aspects: different candidate region settings,adjustment of loss function and improvement of non-maximum suppression algorithm in prediction process.The improved network performance transformation,select the appropriate parameter settings and loss function.The three improvements alleviated the problem of unbalanced positive and negative samples in the end-to-end network,which improved the network recall rate.The training of the convolutional network also has a huge impact on the final effect of the algorithm.In order to train the neural network,this paper attempts a variety of methods to improve the training effect,including pre-training and migration learning for the network,augmentation of the training data set(inversion,scale transformation,grayscale transformation,image blending)and multi-GPU Adjustment of training parameters,etc.This paper has been trained and tested on public datasets and compared with related deep learning-based target detection algorithms.The results show that the algorithm has certain advantages in the comprehensive performance of detection accuracy and detection speed,and also analyzes the shortcomings of the algorithm and the direction of continuous improvement.