The material attributes of foreign object debris (FOD) are the most crucial factors to understand the level of damage sustained by an aircraft. However, the prevalent FOD detection systems lack an effective method for automatic material recognition. This paper proposes a novel FOD material recognition approach based on both transfer learning and a mainstream deep convolutional neural network (D-CNN) model. To this end, we create an FOD image dataset consisting of images from the runways of Shanghai Hongqiao International Airport and the campus of our research institute. We optimize the architecture of the D-CNN by considering the characteristics of the material distribution of the FOD. The results show that the proposed approach can improve the accuracy of material recognition by 39.6% over the state-of-the-art method. The work here will help enhance the intelligence capability of future FOD detection systems and encourage other practical applications of material recognition technology.

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Foreign object debris material recognition based on convolutional neural networks

This paper reports about potentials of hyperspectral imaging for object detection, especially on an application of foreign object detection (FOD) in meat products. A sequential deep-learning framework is proposed by using region-proposal networks (RPNs) and 3D convolutional networks (CNNs). Two independent datasets of images, contaminated with many types of foreign materials, were used for training and testing the proposed model. Results show that the proposed RPN model outperforms a selected search method in terms of accuracy, efficiency, and run-time. An FOD model based on RPN and 3D-CNN, or selected search with a 3D-CNN solve FOD with an average precision of 81.0% or 50.6%, respectively. This study demonstrates opportunities when using hyperspectral imaging systems for real-time object detection by using both spectral and spatial features combined.

A Sequential CNN Approach for Foreign Object Detection in Hyperspectral Images

In this paper, we propose a novel framework of Foreign Object Debris (FOD) classification combining scale-invariant feature transform (SIFT) feature and color feature. This system contains FOD detection subsystem, image quality assessment, control center and FOD recognition subsystem. The system not only achieves the goal of FOD detection, but also fulfills the task of FOD classification. We propose a mixed feature method that combines SIFT feature and color feature to extract FOD feature and use Support vector machine (SVM) or nearest neighbor (NN) to classify FOD image. Experiment results show that the proposed framework is effective and accurate.

Fusion of low-level feature for FOD classification

In this paper, we develop a novel algorithm for classifying foreign object debris (FOD) based on the integrated visual features and extreme learning machine (ELM). After image preprocessing, various types of characteristics of the FOD image such as the color names, the scale-invariant feature transform (SIFT), and the histograms of oriented gradient (HOG) features are extracted in the proposed algorithm. These features are then combined into an integrated visual feature vector to characterize foreign objects in the image. Further, according to the extracted integrated visual features, classification is carried out using the ELM classifier. The experimental results show that the proposed classification algorithm outperforms other state-of-the-art methods. Furthermore, we also demonstrate the effectiveness of the use of the integrated visual features and the ELM classifier.

Classification of Foreign Object Debris Using Integrated Visual Features and Extreme Learning Machine

In this paper, we propose a novel framework of Foreign Object Debris (FOD) classification system. The system contains a FOD detection subsystem, electro-optical subsystem and the control center. The system not only provides continuous surveillance of scanned surfaces and achieves the goal of FOD detection, but also performs FOD classification. Both low level features and subspace features are compared to extract the FOD. Multiclass classifiers are trained in all the candidate feature spaces with the Support Vector Machine (SVM) to classify FOD. Experimental results show that it is promising to classify FOD with low-level features.

Yandan Zheng

Papers

A Novel FOD Classification System Based on Visual Features