CLUTTER AWARE DEEP DETECTION FOR SUBSURFACE RADAR TARGETS

The clutter encounters in Ground Penetrating Radar (GPR) systems decrease the performance of target detection methods. This work presents a clutter aware detection method using deep learning. The clutter is learned and eliminated prior to the detection by a low rank and sparse decomposition of the raw data matrix. The deep networks are fed with clutter free data with increased target visibility. GPR scenarios are generated by gprMax. Recently proposed robust non-negative matrix factorization (RNMF) with less complexity and better visual performance among low rank and sparse decomposition (LRSD) methods, performs the clutter removal. Besides the traditional Faster R-CNN, Yolo5 and EfficientDet are used in the detection step. Results validate that using clutter removed data increases the detection rate of deep networks.