Visual detection, recognition, and classification of surface-buried UXO based on soft-computing decision fusion

Abstract

In this paper, we have addressed the problem of visual inspection, recognition, and discrimination of UXO based on computer vision techniques and introduced three complimentary color, texture, and shape classifiers. The proposed technique initially enhances an image taken from an UXO site and removes terrain background. Next, it applies a blob detector to detect the salient objects of the environment. The UXO classification begins with a perceptive color classifier that classifies the found salient objects based on their color hues. The color classifier attempts to differentiate and classify the color of salient objects based on the color hue information of some known UXO objects in the database. A color ranking scheme is applied for ranking color hue likelihood of the salient objects in the environment. Next, an intuitive texture classifier is applied to characterize the surface texture of the salient objects. The texture signature is used to disjointedly discriminate objects whose surface texture properties matching the priori known UXO textures. Lasting, an intuitive Object Shape Classifier is applied to independently arbitrate the classification of the UXO. Three soft computing methods were developed for robust decision fusion of three UXO feature classifiers. These soft computing techniques include: a statistical-based genetic algorithm, a hamming neural network, and a fuzzy logic algorithm. In this paper, we present details of the UXO feature classifiers and discuss the performance of three decision fusion methods for fusion of results from the three UXO feature classifiers. The main contributing factor of this work is toward designing an ultimate fully-automated tele-robotic system for UXO classification and decontamination.