Pattern Orientation Finder (POF): A Robust, Bio-Inspired Light Algorithm for Pattern Orientation Measurement

Abstract

We present the Pattern Orientation Finder (POF), an innovative, bio-inspired algorithm for measuring the orientation of patterns of parallel elements. The POF was developed to obtain an autonomous navigation system for drones inspecting vegetable cultivations. The main challenge was to obtain an accurate and reliable measurement of orientation despite the high level of noise that characterizes aerial views of vegetable crops. The POF algorithm is computationally light and operable on embedded systems. We assessed the performance of the POF algorithm using images of different cultivation types. The outcomes were examined in light of the accuracy and reliability of the measurement; special attention was paid to the relationship between performance and parameterization. The results show that the POF guarantees excellent performance, even in more challenging conditions. The POF shows high reliability and robustness, even in high-noise contexts. Finally, tests on images from different sectors suggest that the POF has excellent potential for application to other fields as well.