Advanced Automatic Target Recognition (ATR) with Infrared (IR) Sensors
Hai- Wen Chen, Neal Gross, Ravi Kapadia, Joseph Cheah, Mo Gharbieh
Abstract
Automatic Target Detection (ATD) and Recognition (ATR) are critical for video analysis and image understanding for many military and commercial applications deployed on satellites and UAV platforms. Infrared (IR) sensors can be used to detect targets during day and night time but there are few effective ATR algorithms that can exploit these sensors. Several years ago, Defense Systems Information Analysis Center (DSIAC) released an ATR Algorithm Development Image Database containing a large collection of mid-wave infrared (MWIR) imagery with multiple military and civilian vehicles as labelled targets. With the DSIAC database, we have developed AI models, which combine layers of the open-source You Only Look Once (YOLOv2) detection model with customized Convolutional Neural Network (CNN) feature extraction layers. The CNN layers are trained to extract the key features of the vehicle targets from the IR images. YOLOv2 provides target detection, classification, and localization. We have trained and tested the CNN YOLOv2 models with four different military and civilian vehicles (T72, ZSU -23-4, SUV, pickup truck) at distances ranging from 2,000m to 5,000m. Our ATR results with IR datasets show high mean average precision (mAP) between 97.25%-99.5% for day and night time images at distances of 2,000m to 5,000m. That is, we can both reliably detect and recognize different targets with only a few missed detections, and without a falsely recognized target (e.g., mistakenly classifying a civilian vehicle as a military vehicle) from as far as 5,000m. This work represents significant progress in being able to perform ATR at all times (day and night).