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arXiv · 2210.00755

D\'etection de petites cibles par apprentissage profond et crit\`ere a contrario

Alina Ciocarlan·Sylvie Le Hegarat-Mascle·Sidonie Lefebvre·Clara Barbanson
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Abstract

Small target detection is an essential yet challenging task in defense applications, since differentiating low-contrast targets from natural textured and noisy environment remains difficult. To better take into account the contextual information, we propose to explore deep learning approaches based on attention mechanisms. Specifically, we propose a customized version of TransUnet including channel attention, which has shown a significant improvement in performance. Moreover, the lack of annotated data induces weak detection precision, leading to many false alarms. We thus explore a contrario methods in order to select meaningful potential targets detected by a weak deep learning training. -- La d\'etection de petites cibles est une probl\'ematique d\'elicate mais essentielle dans le domaine de la d\'efense, notamment lorsqu'il s'agit de diff\'erencier ces cibles d'un fond bruit\'e ou textur\'e, ou lorsqu'elles sont de faible contraste. Pour mieux prendre en compte les informations contextuelles, nous proposons d'explorer diff\'erentes approches de segmentation par apprentissage profond, dont certaines bas\'ees sur les m\'ecanismes d'attention. Nous proposons \'egalement d'inclure un module d'attention par canal au TransUnet, r\'eseau \`a l'\'etat de l'art, ce qui permet d'am\'eliorer significativement les performances. Par ailleurs, le manque de donn\'ees annot\'ees induit une perte en pr\'ecision lors des d\'etections, conduisant \`a de nombreuses fausses alarmes non pertinentes. Nous explorons donc des m\'ethodes a contrario afin de s\'electionner les cibles les plus significatives d\'etect\'ees par un r\'eseau entra\^in\'e avec peu de donn\'ees.

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Alina Ciocarlan, Sylvie Le Hegarat-Mascle, Sidonie Lefebvre, Clara Barbanson. 2022-10-03. D\'etection de petites cibles par apprentissage profond et crit\`ere a contrario. https://arxiv.org/abs/2210.00755

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