Search

Showing total 2 results
2 results

1. Enhancing computer image recognition with improved image algorithms.

Author

Huang, Lanqing, Yao, Cheng, Zhang, Lingyan, Luo, Shijian, Ying, Fangtian, and Ying, Weiqiang

Subjects
IMAGE recognition (Computer vision), IMAGE processing, ALGORITHMS, COMPUTERS, COMPUTER algorithms
Abstract

Advances in computer image recognition have significantly impacted many industries, including healthcare, security and autonomous systems. This paper aims to explore the potential of improving image algorithms to enhance computer image recognition. Specifically, we will focus on regression methods as a means to improve the accuracy and efficiency of identifying images. In this study, we will analyze various regression techniques and their applications in computer image recognition, as well as the resulting performance improvements through detailed examples and data analysis. This paper deals with the problems related to visual image processing in outdoor unstructured environment. Finally, the heterogeneous patterns are converted into the same pattern, and the heterogeneous patterns are extracted from the fusion features of data modes. The simulation results show that the perception ability and recognition ability of outdoor image recognition in complex environment are improved. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

2. Automatic Localization and Count of Agricultural Crop Pests Based on an Improved Deep Learning Pipeline

Author

Peng Chen, Weilu Li, Bing Wang, and Chengjun Xie

Subjects
0301 basic medicine, Crops, Agricultural, Insecta, Computer science, Pipeline (computing), Science, Feature extraction, Image processing, Convolutional neural network, Models, Biological, Residual neural network, Article, Convolution, 03 medical and health sciences, 0302 clinical medicine, Deep Learning, Image Processing, Computer-Assisted, Animals, Multidisciplinary, business.industry, Deep learning, Computational science, Pattern recognition, 030104 developmental biology, Feature (computer vision), Medicine, Artificial intelligence, business, 030217 neurology & neurosurgery, Algorithms
Abstract

Insect pests are known to be a major cause of damage to agricultural crops. This paper proposed a deep learning-based pipeline for localization and counting of agricultural pests in images by self-learning saliency feature maps. Our method integrates a convolutional neural network (CNN) of ZF (Zeiler and Fergus model) and a region proposal network (RPN) with Non-Maximum Suppression (NMS) to remove overlapping detections. First, the convolutional layers in ZF Net, without average pooling layer and fc layers, were used to compute feature maps of images, which can better retain the original pixel information through smaller convolution kernels. Then, several critical parameters of the method were optimized, including the output size, score threshold, NMS threshold, and so on. To demonstrate the practical applications of our method, different feature extraction networks were explored, including AlexNet, ResNet and ZF Net. Finally, the model trained on smaller multi-scale images was tested on original large images. Experimental results showed that our method achieved a precision of 0.93 with a miss rate of 0.10. Moreover, our model achieved a mean Accuracy Precision (mAP) of 0.885.

Published
2019