Faster Regional Convolutional Neural Network for automated detection of PFM-1 butterfly mine in orthophotos

Overview
In our study, we deployed the Faster Regional-CNN (Faster R-CNN) [26]. This type of CNN has successful applications across the field of remote sensing from detecting maize tassels to airplanes to gravity waves [27,28,29]. We chose this type of CNN because of its superior speed and accuracy in detecting small objects to R-CNNs [30], Fast R-CNNs [31], Spatial Pyramid Pooling-Nets [32], and “You Only Look Once” (YOLO) Networks [33,34,35].
To automate the detection and mapping of the PFM-1 landmines, the CNN was trained and tested two separate times. The first time, the training data consisted of 165 RGB images obtained from different crops of six orthophotos. The orthophotos consisted of three flights over the same 10 × 20 m rubble environment and three flights over the same 10 × 20 m grass environment. Both the grass and rubble datasets were taken in fall 2019 and have 28 PFM-1 mines, four KSF-Casings, and two KSF-Caps scattered throughout the field. All training and testing was done on a Dual Socket Intel(R) Xeon(R) Silver 4114 CPU @ 2.20 GHz with 128 GB of RAM with a Titan V GPU with 12 GB of RAM. The CNN took 37 min to train over 50 epochs.
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