Title :

Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images

Author(s) :

Berg, Paul [Auteur]
Observation de l’environnement par imagerie complexe [OBELIX]
Institut de Recherche en Informatique et Systèmes Aléatoires [IRISA]
Santana Maia, Deise [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Pham, Minh-Tan [Auteur]
Observation de l’environnement par imagerie complexe [OBELIX]
Institut de Recherche en Informatique et Systèmes Aléatoires [IRISA]
Lefèvre, Sébastien [Auteur]
Observation de l’environnement par imagerie complexe [OBELIX]
Institut de Recherche en Informatique et Systèmes Aléatoires [IRISA]

Journal title :

Remote Sensing

Pages :

19

Publisher :

MDPI

Publication date :

2022-01-12

ISSN :

2072-4292

English keyword(s) :

anomaly detection
deep learning
weakly supervised learning
convolutional neural networks

HAL domain(s) :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Informatique [cs]/Traitement des images [eess.IV]

English abstract : [en]

Human activities in the sea, such as intensive fishing and exploitation of offshore wind 1 farms, may impact negatively on the marine mega fauna. As an attempt to control such impacts, 2 surveying, and tracking of marine ...
Show more >Human activities in the sea, such as intensive fishing and exploitation of offshore wind 1 farms, may impact negatively on the marine mega fauna. As an attempt to control such impacts, 2 surveying, and tracking of marine animals are often performed on the sites where those activities 3 take place. Nowadays, thank to high resolution cameras and to the development of machine 4 learning techniques, tracking of wild animals can be performed remotely and the analysis of the 5 acquired images can be automatized using state-of-the-art object detection models. However, 6 most state-of-the-art detection methods require lots of annotated data to provide satisfactory 7 results. Since analyzing thousands of images acquired during a flight survey can be a cumbersome 8 and time consuming task, we focus in this article on the weakly supervised detection of marine 9 animals. We propose a modification of the patch distribution modeling method (PaDiM), which is currently one of the state-of-the-art approaches for anomaly detection and localization for visual industrial inspection. In order to show its effectiveness and suitability for marine animal detection, we conduct a comparative evaluation of the proposed method against the original version, as well as other state-of-the-art approaches on two high-resolution marine animal image datasets. On both tested datasets, the proposed method yielded better F1 and recall scores (75% recall/41% precision, and 57% recall/60% precision, respectively) when trained on images known to contain no object of interest. This shows a great potential of the proposed approach to speed up the marine animal discovery in new flight surveys. Additionally, such a method could be adopted for bounding box proposals to perform faster and cheaper annotation within a fully-supervised detection framework.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL