Hyperparameters Tuning of Faster R-CNN Deep Learning Transfer for Persistent Object Detection in Radar Images

Gonzales-Martinez R.; Machacuay J.; Rotta P.; Chinguel C. · IEEE Latin America Transactions (2022) · DOI: 10.1109/tla.2022.9675474

Resumen

In previous work, a methodology was proposed to obtain a sea surface object detection model based on the FasterR-CNN architecture using Sperry Marine commercial navigation radar images. Unfortunately, the percentage of recall using the validation dataset was 75.76% with a minimum score for true positives of 7% due to a network overfitting problem. In this research, the overfitting problem is solved by comparing three experiments. Each experiment consists of the combinations of different hyperparameters within the Faster RCNN architecture. The main hyperparameters modified to improve the performance of the model were weights initialization and the optimizer. The results finally achieved, show a significant improvement in relation to the previous work. The improved persistent object detection model shows a recall of 93.94% with a minimum score for true positives of 98%.

Instituciones: Universidad de Piura