# Deep Learning-Assisted Autofocus for Aerial Cameras in Maritime Photography

## TL;DR

This paper introduces a deep learning-based autofocus system for aerial cameras to improve focusing in low-contrast maritime environments.

## Contribution

A novel autofocus system combining deep learning and traditional search methods for aerial cameras in maritime settings.

## Key findings

- The system improves focusing speed by approximately 60% compared to traditional methods.
- It achieves high accuracy and strong adaptability in low-contrast maritime conditions.
- Using a built-in calibration target enhances overall imaging performance.

## Abstract

To address the unreliable autofocus problem of drone-mounted visible-light aerial cameras in low-contrast maritime environments, this paper proposes an autofocus system that combines deep-learning-based coarse focusing with traditional search-based fine adjustment. The system uses a built-in high-contrast resolution test chart as the signal source. Images captured by the imaging sensor are fed into a lightweight convolutional neural network to regress the defocus distance, enabling fast focus positioning. This avoids the weak signal and inaccurate focusing often encountered when adjusting focus directly on low-contrast sea surfaces. In the fine-focusing stage, a hybrid strategy integrating hill-climbing search and inverse correction is adopted. By evaluating the image sharpness function, the system accurately locks onto the optimal focal plane, forming intelligent closed-loop control. Experiments show that this method, which combines imaging of the built-in calibration target with deep-learning-based coarse focusing, significantly improves focusing efficiency. Compared with traditional full-range search strategies, the focusing speed is increased by approximately 60%. While ensuring high accuracy and strong adaptability, the proposed approach effectively enhances the overall imaging performance of aerial cameras in low-contrast maritime conditions.