Detection of Tomato Ripening Stages using Yolov3-tiny

One of the most important agricultural products in Mexico is the tomato (Solanum lycopersicum), which occupies the 4th place national most produced product . Therefore, it is necessary to improve its production, building automatic detection system that detect, classify an keep tacks of the fruits is one way to archieve it. So, in this paper, we address the design of a computer vision system to detect tomatoes at different ripening stages. To solve the problem, we use a neural network-based model for tomato classification and detection. Specifically, we use the YOLOv3-tiny model because it is one of the lightest current deep neural networks. To train it, we perform two grid searches testing several combinations of hyperparameters. Our experiments showed an f1-score of 90.0% in the localization and classification of ripening stages in a custom dataset.

A Comparison of Tiny-nerf versus Spatial Representations for 3d Reconstruction

Neural rendering has emerged as a powerful paradigm for synthesizing images, offering many benefits over classical rendering by using neural networks to reconstruct surfaces, represent shapes, and synthesize novel views, either for objects or scenes. In this neural rendering, the environment is encoded into a neural network. We believe that these new representations can be used to codify the scene for a mobile robot. Therefore, in this work, we perform a comparison between a trending neural rendering, called tiny-NeRF, and other volume representations that are commonly used as maps in robotics, such as voxel maps, point clouds, and triangular meshes. The target is to know the advantages and disadvantages of neural representations in the robotics context. The comparison is made in terms of spatial complexity and processing time to obtain a model. Experiments show that tiny-NeRF requires three times less memory space compared to other representations. In terms of processing time, tiny-NeRF takes about six times more to compute the model.

Spiral Trajectories for Building Inspection with Quadrotors

Inspection of large building is an important task since it can prevent material and human losses. A cheap and fast way to do the inspections is by sensors mounted on quadrotor vehicles. The challenge here is to compute a trajectory so that the building is completely observed while this same trajectory can be followed by the quadrotor in a smooth way. To address the problem, we propose a method that receives a 2.5D model of the target building and computes a smooth trajectory that can be followed by the quadrotor controller. The computed trajectory is a Fourier series that matches the desired behaviour. Our method has been tested in simulation and we have compared it against polynomial trajectories. Our result show that the method is efficient and can be applied to different building shapes.