# Deep-NFA: a Deep $\\textit{a contrario}$ Framework for Small Object Detection

Source: [Deep-NFA: a Deep $\\textit{a contrario}$ Framework for Small Object Detection](<https://www.sota2.com/research/paper/68fa721b1e209131cea54843>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:18:23.013Z

Authors: Alina Ciocarlan, Sylvie Le Hegarat-Mascle, Sidonie Lefebvre, Arnaud Woiselle

Published: 2023-03-02T15:48:02.000Z

## Abstract

The detection of small objects is a challenging task in computer vision. Conventional object detection methods have difficulty in finding the balance between high detection and low false alarm rates. In the literature, some methods have addressed this issue by enhancing the feature map responses, but without guaranteeing robustness with respect to the number of false alarms induced by background elements. To tackle this problem, we introduce an $\\textit{a contrario}$ decision criterion into the learning process to take into account the unexpectedness of small objects. This statistic criterion enhances the feature map responses while controlling the number of false alarms (NFA) and can be integrated into any semantic segmentation neural network. Our add-on NFA module not only allows us to obtain competitive results for small target and crack detection tasks respectively, but also leads to more robust and interpretable results.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2303.01363>)

[PDF](<https://arxiv.org/pdf/2303.01363.pdf>)