Runway numbers are based on1/21/2024 ![]() Men ZC, Jiang J, Guo X, Chen LJ, Liu DS (2020) Airport runway semantic segmentation based on dcnn in high spatial resolution remote sensing images. In: Digital image computing:, techniques and applications, pp 1–8 IEEE Geosci Remote Sens Lett 10(3):471–475Īkbar J, Shahzad M, Malik MI, Ul-Hasan A, Shafait F (2019) Runway detection and localization in aerial images using deep learning. Springer, pp 210–218Īytekin Ö, Zöngür U, Halici U (2013) Texture-based airport runway detection. In: International Conference on Genetic and Evolutionary Computing. Appl Intell 52(1):564–579Ĭang Y, Chen C, Qiao Y (2019) Research on the application of instance segmentation algorithm in the counting of metro waiting population. Zhang X-L, Du B-C, Luo Z-C, Ma K (2022) Lightweight and efficient asymmetric network design for real-time semantic segmentation. Sun G, Wen Y, Li Y (2022) Instance segmentation using semi-supervised learning for fire recognition. Tong H, Fang Z, Wei Z, Cai Q, Gao Y (2021) Sat-net: a side attention network for retinal image segmentation. In: IEEE 2nd International Conference on Electronic Technology, Communication and Information. Zhang L, Wang J, An Z, Shang Y (2022) Runway image recognition technology based on line feature. Tang G, Xiao Z, Liu Q, Liu H (2015) A novel airport detection method via line segment classification and texture classification. Zhang Z, Zou C, Han P, Lu X (2020) A runway detection method based on classification using optimized polarimetric features and hog features for polsar images. In: 2022 IEEE International Conference on Unmanned Systems (ICUS), pp 891–895 Sun X, Zhang Z, Liu J, Wang Q, Zhou J (2022) Visual pose measurement for automatic landing on an aircraft carrier. Krammer C, Scherer S, Mishra C, Holzapfel F (2021) Concept for a vision-augmented automatic landing system for vtol aircraft. Krammer C, Mishra C, Holzapfel F (2020) Testing and evaluation of a vision-augmented navigation system for automatic landings of general aviation aircraft. Kügler ME, Mumm NC, Holzapfel F, Schwithal A, Angermann M (2019) Vision-augmented automatic landing of a general aviation fly-by-wire demonstrator. IEEE J Sel Top Appl Earth Obs Remote Sens 15:6671–6686 Wang D, Zhang F, Ma F, Hu W, Tang Y, Zhou Y (2022) A benchmark sentinel-1 sar dataset for airport detection. IEEE J Sel Top Appl Earth Obs Remote Sens 14:314–326 Tu J, Gao F, Sun J, Hussain A, Zhou H (2020) Airport detection in sar images via salient line segment detector and edge-oriented region growing. ![]() SPM and CPCL can effectively enhance the AS metric while modestly improving accuracy. The experiments show that existing instance segmentation methods can achieve prediction results with good performance on BARS. Furthermore, a novel evaluation metric named average smoothness (AS) is developed to measure smoothness. Based on the characteristic of an airport runway with a regular shape, we propose a plug-and-play smoothing postprocessing module (SPM) and a contour point constraint loss (CPCL) function to smooth segmentation results for mask-based and contour-based methods, respectively. We evaluate eleven representative instance segmentation methods on BARS and analyze their performance. The dataset, which was collected using the X-Plane simulation platform, contains 10,256 images and 30,201 instances with three categories. BARS has the largest dataset with the richest categories and the only instance annotation in the field. ![]() Additionally, a semiautomatic annotation pipeline is designed to reduce the annotation workload. Therefore, we propose a benchmark for airport runway segmentation, named BARS. However, the lack of large-scale, publicly available datasets in this field makes the development of methods based on DL difficult. With the rapid development of deep learning (DL), related methods achieve good performance on segmentation tasks and can be well adapted to complex scenes. Airport runway segmentation can effectively reduce the accident rate during the landing phase, which has the largest risk of flight accidents. ![]()
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