With the tremendous growth of the aviation sector, aviation safety becomes increasingly prominent and generally worrisome. Aircraft safety issues exist not just during flight, but also during take-off and landing. Airport runway flaws like cracks and potholes, as well as runway foreign matter (FOD), provide significant concealed threats that risk aircraft take-off and landing, small dips on the runway might cause the aircraft to deviate, and foreign objects on the runway can puncture the tires, causing it to explode. Airport runway safety detection is critical. Remote sensing has a vital role in detecting and monitoring an area's physical qualities at a distance. It required high-accuracy cameras to acquire remotely sensed photos which are used in the detection of cracks on the runway surface. These cracks are one of the first signs of structural deterioration, which is crucial for maintenance, and used in upgrading the runway condition. Fracture identification on the surface of a runway by automated analysis of optical images. Firstly, canny edge detection is used to detect the cracks in the acquired images. Secondly, morphological operations were made to enhance the result of canny edge detection such as opening, closing, Skeletonization, Boundary extraction, and others. Thirdly, the analysis of the results was made to compare the length, average width, and area of the cracks to the manual detection of the visualized cracks. The proposed algorithm was found to be highly useful for analyzing surface fractures as well as classifying them, which gave high accuracy results of imaged edge detection.