Performance Comparison of Convolutional Neural Network Models for Object Detection in Tethered Balloon Imagery
Debora F. Dos Santos, André O. Françani, Marcos R. O. A. Máximo, Arthur Schveitzer Ferreira · 2021
The growing field of remote sensing requires object detection solutions. This work compares the performance of different neural networks for detecting vehicle, person, and boat objects in tethered balloon imagery. Four state-of-the-art architectures were chosen – Faster R-CNN, SSD, Retinanet, and YOLO. For training, the algorithms apply transfer learning, backbone variation, and hyper-parameter tuning. The evaluation utilizes the mean average precision and the inference time as metrics. The YOLOv4 416x model achieved the best trade-off between speed and accuracy with a mAP50of 90.3%.