MRTD: man versus machine

Rheenen, Arthur D. van; Taule, Petter; Thomassen, Jan B.; Madsen, Eirik Blix

doi:10.1117/12.2304581

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of the drawbacks of traditional MRTD measurement methods, other alternative methods have been pursued by some researchers. The studies in [13,14] discussed the possibilities for objective measurements and alternative MRTD measurement methods. Some researchers [15][16][17] used an indirect method to evaluate the MRTD by computing the modulation transfer function and the noise equivalent temperature difference (NETD) of the thermal imager.…”

Section: Related Work Mrtd Measurement Developmentmentioning

confidence: 99%

Multiscale Deblurred Feature Extraction Network for Automatic Four-Rod Target Detection in MRTD Measuring Process of Thermal Imagers

Guo

Guan

2023

Sensors

View full text Add to dashboard Cite

The minimum resolvable temperature difference (MRTD) at which a four-rod target can be resolved is a critical parameter used to assess the comprehensive performance of thermal imaging systems, which is important for technological innovation in military and other fields. Recently, there have been some attempts to use an automatic objective approach based on deep learning to take the place of the classical manual subjective MRTD measurement approach, which is strongly affected by the psychological subjective factors of the experimenter and is limited in accuracy and speed. However, the scale variability of four-rod targets and the low pixels of infrared thermal cameras have turned out to be a challenging problem for automatic MRTD measurement. We propose a multiscale deblurred feature extraction network (MDF-Net), a backbone based on a yolov5 neural network, in an attempt to solve the aforementioned problem. We first present a global attention mechanism (GAM) attention module to represent strong images of the four-rod targets. Next, a Rep VGG module is introduced to decrease the blur. Our experiments show that the proposed method achieves the desired effect and state-of-the-art detection results, which innovatively improve the accuracy of four-rod target detection to 82.3% and thus make it possible for the thermal imagers to see further and to respond faster and more accurately.

show abstract