Object Detection in Rural Roads Through SSD and YOLO Framework
Abstract:
Object detection is challenging in the computer vision area and is crucial in autonomous driving systems. The largest number of traditional techniques or the use of deep learning are evaluated in the urban area, but in rural areas, there is little research carried out. The goal of this work is object detection in rural roads, this paper presents the use of deep learning frameworks used as You Only Look Once (YOLO) and another that belongs to the same category of one-stage is the well-known Single Shot Multi-Box Detector (SSD), in the state of the literature, produces excellent results in detecting objects in real-time. These models behave differently in network architecture, for this reason, we use images of rural roads with different environments to achieve an optimal balance between precision and precision in the detection of objects. Furthermore, as there is no dataset in these environments, we created our own data set to perform the experiments due to the difficulty of this problem. The result of both detectors has produced acceptable results under certain conditions like lighting conditions, viewing perspectives, partial occlusion of the object.
Año de publicación:
2021
Keywords:
- deep learning
- Rural roads
- object detection
- Computer Vision
Fuente:


Tipo de documento:
Conference Object
Estado:
Acceso restringido
Áreas de conocimiento:
- Visión por computadora
- Simulación por computadora
Áreas temáticas:
- Métodos informáticos especiales
- Ciencias de la computación
- Física aplicada