A Semi-supervised Object Detection Algorithm for Underwater Imagery

• URL: http://arxiv.org/abs/2306.04834v1
• Date: Wed, 7 Jun 2023 23:40:04 GMT
• Title: A Semi-supervised Object Detection Algorithm for Underwater Imagery
• Authors: Suraj Bijjahalli, Oscar Pizarro, and Stefan B. Williams
• Abstract summary: We propose to treat artificial objects as anomalies and detect them through a semi-supervised framework based on Variational Autoencoders (VAEs) We develop a method which clusters image data in a learned low-dimensional latent space and extracts images that are likely to contain anomalous features. We demonstrate that by applying both methods on large image datasets, human operators can be shown candidate anomalous samples with a low false positive rate to identify objects of interest.
• Score: 10.017195276758455
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Detection of artificial objects from underwater imagery gathered by Autonomous Underwater Vehicles (AUVs) is a key requirement for many subsea applications. Real-world AUV image datasets tend to be very large and unlabelled. Furthermore, such datasets are typically imbalanced, containing few instances of objects of interest, particularly when searching for unusual objects in a scene. It is therefore, difficult to fit models capable of reliably detecting these objects. Given these factors, we propose to treat artificial objects as anomalies and detect them through a semi-supervised framework based on Variational Autoencoders (VAEs). We develop a method which clusters image data in a learned low-dimensional latent space and extracts images that are likely to contain anomalous features. We also devise an anomaly score based on extracting poorly reconstructed regions of an image. We demonstrate that by applying both methods on large image datasets, human operators can be shown candidate anomalous samples with a low false positive rate to identify objects of interest. We apply our approach to real seafloor imagery gathered by an AUV and evaluate its sensitivity to the dimensionality of the latent representation used by the VAE. We evaluate the precision-recall tradeoff and demonstrate that by choosing an appropriate latent dimensionality and threshold, we are able to achieve an average precision of 0.64 on unlabelled datasets.

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