Foreign Object Detection using Hybrid Assessment and Enhancement Technique

In this paper an efficient approach “Hybrid Assessment and Enhancement technique for Foreign Object Detection (HAE-FOD)” is proposed to detect debris in the runway. Foreign objects such as engine fasteners or aircraft parts are left in the runway when an aircraft takes off. These objects may cause damage to life and property when they are not detected. In practice, they are identified manually which is tiresome and not always accurate. Computer vision using image processing can be of aid to detect the foreign objects. So, an automated system for detection of foreign objects in runway is proposed in this paper. It facilitates accurate foreign object detection under varying lighting and environment conditions. Images from runway are assessed for their quality and enhanced for detection of foreign objects. Images are assessed to determine the quality using the combined Image Quality Assessment Techniques of Histogram, SSIM (Structural Similarity Index Measure) and PSNR Ratio. Then, the image quality is improved using Enhancement Techniques with the combination of Gamma Correction, CLAHE (Contrast Limited Adaptive Histogram Equalization) and Wiener Filter. To identify Foreign Objects from the enhanced images, Image Segmentation Techniques such as Edge Detection, Background Subtraction and Temporal Differencing are used. The proposed hybrid system with the combination of enhancement techniques based on type of degradation provides a better way for object detection in runway.