There are several ways in which Foreign Object Debris (FOD) are detected on runways. Some of these methods include Radar, infrared technologies, and stationary cameras mounted on the runway and use image processing tools to find these FODs. Radar technology is highly accurate when finding FODs but is highly inaccurate with small FOD items causing a high false-positive rate. Stationary based RGB camera-based methods also have a high false-positive rate prompting the shutdown of runways, creating disruptions for both the airport and the airline carriers. The paper presents a new method of detection by using an Unmanned Aerial Vehicle (UAV) to fly above the runway at a low altitude (e.g. < 30 m) to find FOD in. We developed a system that combines a UAV, an RGB camera, an Artificial Intelligence (AI) detector trained using deep learning methods and locally collected images over a runway. The classes specifically looked at were paper, metal, bolts, plastic, and plastic bottles. Different lighting conditions of both full sunlight and cloudy weather were taken into consideration when the images were collected. The detector was trained with various data augmentation techniques including resize, rotate, and colour augmentation. Results have concluded that there is a potential use for UAV's as a method of FOD detection, with a high rate of accuracy in the detections. This could lead to shorter timeframes and fewer disruptions where runways are closed.

UAV and AI Application for Runway Foreign Object Debris (FOD) Detection

In recent years, aviation security has become an important area of concern as foreign object debris (FOD) on the airport pavement has a huge potential risk to aircraft during takeoff and landing. Therefore, accurate detection of FOD is important to ensure aircraft flight safety. This paper proposes a novel method to detect FOD based on random forest. The complexity of information in airfield pavement images and the variability of FOD make FOD features difficult to design manually. To overcome this challenge, this study designs the pixel visual feature (PVF), in which weight and receptive field are determined through learning to obtain the optimal PVF. Then, the framework of random forest employing the optimal PVF to segment FOD is proposed. The effectiveness of the proposed method is demonstrated on the FOD dataset. The results show that compared with the original random forest and the deep learning method of Deeplabv3+, the proposed method is superior in precision and recall for FOD detection. This work aims to improve the accuracy of FOD detection and provide a reference for researchers interested in FOD detection in aviation.

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Foreign Object Debris Detection for Optical Imaging Sensors Based on Random Forest

Foreign object debris (FOD) on airport runways can cause serious accidents and huge economic losses FOD detection systems based on millimeter-wave (MMW) radar sensors have the advantages of higher range resolution and lower power consumption However, it is difficult for traditional FOD detection methods to detect and distinguish weak signals of targets from strong ground clutter To solve this problem, this paper proposes a new FOD detection approach based on optimized variational mode decomposition (VMD) and support vector data description (SVDD) This approach utilizes SVDD as a classifier to distinguish FOD signals from clutter signals More importantly, the VMD optimized by whale optimization algorithm (WOA) is used to improve the accuracy and stability of the classifier The results from both the simulation and field case show the excellent FOD detection performance of the proposed VMD-SVDD method

https://www.mdpi.com/1424-8220/21/3/997/pdf

A FOD Detection Approach on Millimeter-Wave Radar Sensors Based on Optimal VMD and SVDD.

Foreign Object Debris (FOD) refers to any foreign material on the airfield that may injure and threaten the aircraft and airport system. Due to the complex background on the airfield pavement and weak target echoes in long-distance monitoring, it is not easy to detect objects of various types and sizes. The existing FOD radar system’s detection method has a short effective range, and the detectable objects’ radar cross-section intensity is no less than −20 dBsm. In this paper, we propose an integrated FOD automatic target detection algorithm for millimeter-wave (MMW) surveillance radar to improve small target detection under long-range conditions of over 660 m. The signal form of FOD and a clutter model of ground clutter received by millimeter-wave radar are primarily utilized and established theoretically. The runway edge detection means that it is employed based on the in-continuity features as the runway region of interest during the automatic extraction step. Following the clutter map constant false alarm detection algorithm, we utilize a time-domain algorithm that functions as the vital detection processor. Moreover, an explicit definition of the FOD detection performance is developed in a characteristic quantitative way. This criterion involves an absolute reference value for all FOD radar systems. The well-designed FOD frequency-modulated continuous-wave MMW surveillance radar is utilized, and actual experiments are carried out in a real airport in Beijing, China. The results validate the proposed method’s effectiveness and the superior performance of FOD target detection in long-range situations.

Foreign Object Debris Automatic Target Detection for Millimeter-Wave Surveillance Radar.

Using millimeter-wave radar to scan and detect small foreign object debris (FOD) on an airport runway surface is a popular solution in civil aviation safety. Since it is impossible to completely eliminate the interference reflections arising from strongly scattering targets or non-homogeneous clutter after clutter cancellation processing, the consequent high false alarm probability has become a key problem to be solved. In this article, we propose a new FOD detection method for interference suppression and false alarm reduction based on an iterative adaptive approach (IAA) algorithm, which is a non-parametric, weighted least squares-based iterative adaptive processing approach that can provide super-resolution capability. Specifically, we first obtain coarse FOD target information by data preprocessing in a conventional detection method. Then, a refined data processing step is conducted based on the IAA algorithm in the azimuth direction. Finally, multiple pieces of information from the two steps above are used to comprehensively distinguish false alarms by fusion processing; thus, we can acquire accurate FOD target information. Real airport data measured by a 93 GHz radar are used to validate the proposed method. Experimental results of the test scene, which include golf balls with a diameter of 43 mm, were placed about 300 m away from radar, which show that the proposed method can effectively reduce the number of false alarms when compared with a traditional FOD detection method. Although metal balls with a diameter of 50 mm were placed about 660 m away from radar, they also can obtain up to 2.2 times azimuth super-resolution capability.

https://www.mdpi.com/1424-8220/21/4/1241/pdf

FOD Detection Method Based on Iterative Adaptive Approach for Millimeter-Wave Radar.

Foreign object debris (FOD) detection can be considered a kind of classification that distinguishes the measured signal as either containing FOD targets or only corresponding to ground clutter. In this paper, we propose a support vector domain description (SVDD) classifier with the particle swarm optimization (PSO) algorithm for FOD detection. The echo features of FOD and ground clutter received by the millimeter-wave radar are first extracted in the power spectrum domain as input eigenvectors of the classifier, followed with the parameters optimized by the PSO algorithm, and lastly, a PSO-SVDD classifier is established. However, since only ground clutter samples are utilized to train the SVDD classifier, overfitting inevitably occurs. Thus, a small number of samples with FOD are added in the training stage to further construct a PSO-NSVDD (NSVDD: SVDD with negative examples) classifier to achieve better classification performance. Experimental results based on measured data showed that the proposed methods could not only achieve a good detection performance but also significantly reduce the false alarm rate.

Small Foreign Object Debris Detection for Millimeter-Wave Radar Based on Power Spectrum Features

The invention discloses TDM-MIMO radar system based on an optimized sparse array and a signal processing method thereof. The system is based on a sparse array, and optimizes array distribution by using a genetic algorithm; and optimized array distribution lowers sidelobe level and improves energy use ratio. The system transmits an LFMCW signal by using a TDM technology without sending an orthogonal signal and matching filtering at a receiving end, thereby making radar system structure less complex. When the number of array elements is specific, DOA performance of the system is superior to thatof a traditional uniform linear array MIMO radar system; under the condition of identical array aperture, the system has the same DOA performance as the uniform linear array MIMO radar system, and has higher angular resolution and more concise structure. Further, the signal calibration processing method provided in the invention can improve accuracy and resolution of DOA estimation of a mobile target by the system effectively under a condition of low signal to noise ratio.

TDM-MIMO radar system based on optimized sparse array and signal processing method thereof

Aiming at the acceleration of the maneuvering target changes uniformly, an adaptive Current Statistical (CS) model based on the estimation of the target motion parameters is proposed. Firstly, the improved CS model is derived by adding the jerk and jerk rate into the state vector, to get a more accurate estimation of the target motion state. Secondly, the distance, speed, acceleration, and jerk obtained by motion parameters estimation are also added to the model measurement. Furthermore, the changes of a jerk in adjacent cycles are compensated by adaptive updating of maneuver frequency. Finally, the filter equation of the improved CS model is introduced. The simulation results show that the proposed model can improve the accuracy of target tracking compared with the model that only measures distance and speed.

An Adaptive Current Statistical Model Based on Estimation of Target Motion Parameters

The invention discloses a fountain leakage detection system comprising the following parts of a terminal monitoring module configured to collect voltages at various locations underwater, judge whethera leakage occurs at the locations according to the voltages, and transmit voltage data and alarm information to the host computer module and cloud server module; a host computer module configured toprocess the data transmitted by the terminal monitoring module, display the underwater electric field distribution state, and judge and early warn the state of the electric fields; and a cloud servermodule configured to store the data input by the terminal monitoring module in the database for users to view in real time, and judge and early warn the state of the electric fields, wherein the hostcomputer module and cloud server module can both be configured to control the movement state of the terminal monitoring module. The system provided by the invention realizes fast and effective detection and early warning of the underwater leakage of the fountain, has the characteristics such as real-time, convenience, speed and safety by combining the host computer and the cloud server to facilitate the operation of the management personnel, and is wide in applicability.

Ni Peishuang

Papers

Small Foreign Object Debris Detection for Millimeter-Wave Radar Based on Power Spectrum Features

TDM-MIMO radar system based on optimized sparse array and signal processing method thereof

An Adaptive Current Statistical Model Based on Estimation of Target Motion Parameters

Fountain leakage detection system