An Object Classification Method Based on Object Characteristic Map

Abstract

The invention relates to an object classification method based on a target characteristic map. Firstly, use laser radar, visible light camera and near-infrared camera to obtain multispectral data and spatial structure data of objects in the area to be detected respectively; then extract the ROI area to be detected; then extract features for each ROI object area, and combine these features into Feature words; finally, the CNN-based deep learning classifier is used to distinguish the feature words to realize fast and reliable classification of objects. The data collected by multiple sensors complement each other, which can effectively avoid the problem of insufficient detection ability of a single sensor; multi-layer spatial feature extraction can describe the characteristics of objects more comprehensively; the classification results of CNN-based deep learning classifiers are more reliable.

Description

technical field [0001] The invention belongs to the field of intelligent video image processing, and in particular relates to an object classification method. Background technique [0002] Object automatic classification technology is widely used in agricultural production, industrial automation, resource recovery and other fields. The object classification method based on machine vision has the advantages of convenient installation, strong adaptability, non-destructive detection, etc., and is a current research hotspot. When there are many types and large numbers of objects to be classified in the scene, and there is a certain degree of mutual occlusion, how to robustly extract each object region and achieve accurate classification is a challenging task. [0003] In response to this problem, the literature [Application of MB-LBP features in visual target detection and classification, Master Thesis of Institute of Automation, Chinese Academy of Sciences, 2008] proposes to u...

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Problems solved by technology

[0003] In response to this problem, the literature [Application of MB-LBP features in visual target detection and classification, Master Thesis of Institute of Automation, Chinese Academy of Sciences, 2008] proposes to use MB-LBP features and EC0C rules to design multi-category target classification algorithms. This method is only applicable to It is used for the classification of large-sized moving objects such as vehicles and human bodies; the literature [Design and Implementation of Fine-grained Object Classification Method, Master Thesis of Beijing Jiaotong University, 2014] proposed an object classification algorithm using convolutional neural network, which realized the classification of horses, cattle, etc. Classification of larger animals such as sheep; patent [object classification method based on improved MFA and transfer learning for small sample sets, CN201510801292.3] discloses a small sample based on improved MFA (Marginal Fisher Analysis) and transfer learning Set (target domain) classification algorithm, which can recognize limited types of objects at the same time; patent [a smart object classification device with recognition function, CN201610089449.9] uses millimeter-wave radar to detect the shape of objects, and the objects to be classified need to be placed on a custom turntable Rotate up and down to achieve 360-degree shape acquisition, which greatly limits its application scenarios; the patent [A method for object classification based on video images, CN201510012901.7] discloses a method for object classification based on video images, including image segmentation, main Component analysis and rectangular saturation feature classification, this method can only better classify the human and vehicle targets in the video image; the patent [a target classification method and system based on the visual bag of words model, CN201410087579.X] by analyzing the sample pictures The position information and description information of feature points realize image classification, which is limited to the classification of specific two-dimensional plane objects

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