Small Vessel Detection in Changing Seaborne Environments Using Anchor-Free Detectors on Aerial Images

Ruiz-Ponce, Pablo; Ortiz-Perez, David; García-Rodríguez, José

doi:10.1007/978-3-031-18050-7_27

Cited by 1 publication

(1 citation statement)

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“…Developing robust and precise models for this application would prove to be highly beneficial in search and rescue missions, humanitarian aid efforts, and surveillance [ 2 , 3 , 4 ] and security operations. However, as we have previously noted in our publication [ 5 ], the primary issue is the high cost of capturing such images and the fact that instances in these images tend to be very small. Additionally, maritime environments provide a large factor of variance that has to be dealt with, and variations among the instances to be detected are equally significant.…”

Section: Introductionmentioning

confidence: 97%

POSEIDON: A Data Augmentation Tool for Small Object Detection Datasets in Maritime Environments

Ruiz-Ponce

Ortiz-Perez

García-Rodríguez

et al. 2023

Sensors

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Certain fields present significant challenges when attempting to train complex Deep Learning architectures, particularly when the available datasets are limited and imbalanced. Real-time object detection in maritime environments using aerial images is a notable example. Although SeaDronesSee is the most extensive and complete dataset for this task, it suffers from significant class imbalance. To address this issue, we present POSEIDON, a data augmentation tool specifically designed for object detection datasets. Our approach generates new training samples by combining objects and samples from the original training set while utilizing the image metadata to make informed decisions. We evaluate our method using YOLOv5 and YOLOv8 and demonstrate its superiority over other balancing techniques, such as error weighting, by an overall improvement of 2.33% and 4.6%, respectively.

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