INTELLIGENT CONTROL OF ROBOTICS AT RODENT BURROW SEGMENTATION USING DEEP CONVOLUTIONAL ARCHITECTURES

In this paper, we investigate the application of neural network architectures for semantic segmentationof rodent burrows for monitoring their population in agricultural fields. In particular, three modelsfor semantic segmentation are considered: convolutional autoencoder (CAE), SegNet, and U-Net. Thesemodels are applied to analyze images obtained from unmanned aerial vehicles (UAVs) and ground roboticmeans, which allows for automatic burrow detection, minimizing the need for labor costs in processinglarge amounts of data. A sample of 247 RGB images containing 1098 labeled burrows was prepared fortraining and testing the models. The quality indicators of semantic segmentation were assessed using theJaccard metric (IoU), which resulted in the following values: 0.511 for CAE, 0.548 for SegNet, and 0.529for U-Net. An assessment of the computational resources required to implement these models in on-boardcomputing units (OCUs) of mobile robotic means was conducted. Two criteria were considered: the numberof floating-point operations (GFLOPS) and the number of model parameters. The results showed thatSegNet requires 2.23 GFLOPS and has 0.76 million parameters, which is 2.58 and 2.33 times less thanSAE and U-Net, respectively. The number of floating-point operations for SegNet was also 2.43 and 1.88times lower than that of SAE and U-Net, respectively. As a result, SegNet outperformed SAE and U-Net inboth segmentation efficiency and required computational resources. This work was carried out as part ofthe implementation of a computer vision system for an agricultural robotic means.