Open Set Recognition of Timber Species Using Deep Learning for Embedded Systems

Abstract

Reliable and rapid identification of timber species is a very relevant issue for many countries in South America and especially for Peru, which is the second country with the largest extend of tropical forest, and that is because this issue is a necessity in order to develop an effective management of the forest resources, such as inspection and control of the timber commerce. Since, current methods of identification are based on a closed set recognition approach, they are not reliable enough to be used in a practical application because scenarios of identification of timber species are by nature an open set recognition problem. For that reason, in this work we propose a convolutional neural network that has two main characteristics, being able to run in a real-time embedded system and being able to handle the open set recognition problem, that is, this model can discriminate between known and unknown species. In order to evaluate it, tests are performed in two timber species datasets and some experiments are developed in the embedded system Raspberry Pi3B+ to measure energy consumption. The results present high metrics, which means that it manages to discriminate the unknown species with high accuracy around 92% for two set of images used. In addition, lower energy consumption (7-10%) and computational resource (5-13%).