# Low-power Ship Detection in Satellite Images Using Neuromorphic Hardware

arXiv: https://arxiv.org/abs/2406.11319 · HTML: https://mlsanity.com/paper/2406.11319/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Fast Single-shot Ship Instance Segmentation Based on Polar Template Mask in Remote Sensing Images](https://mlsanity.com/paper/2008.12447/) (2020)
- [Automated System for Ship Detection from Medium Resolution Satellite Optical Imagery](https://mlsanity.com/paper/2104.13923/) (2021)
- [Estimating the Power Consumption of Heterogeneous Devices when performing AI Inference](https://mlsanity.com/paper/2207.06150/) (2022)

## Built on by (most cited first, 3 of 3)

- [Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems](https://mlsanity.com/paper/2504.00957/) (2025)
- [Efficient Onboard Spacecraft Pose Estimation with Event Cameras and Neuromorphic Hardware](https://mlsanity.com/paper/2604.04117/) (2026)
- [NAVIR: Neuromorphic Audio-Visual Speech Recognition for Robust Human-Robot Interaction on Edge Hardware](https://mlsanity.com/paper/2609.24391/) (2026)

## Abstract (arXiv)

Transmitting Earth observation image data from satellites to ground stations incurs significant costs in terms of power and bandwidth. For maritime ship detection, on-board data processing can identify ships and reduce the amount of data sent to the ground. However, most images captured on board contain only bodies of water or land, with the Airbus Ship Detection dataset showing only 22.1% of images containing ships. We designed a low-power, two-stage system to optimize performance instead of relying on a single complex model. The first stage is a lightweight binary classifier that acts as a gating mechanism to detect the presence of ships. This stage runs on Brainchip's Akida 1.0, which leverages activation sparsity to minimize dynamic power consumption. The second stage employs a YOLOv5 object detection model to identify the location and size of ships. This approach achieves a mean Average Precision (mAP) of 76.9%, which increases to 79.3% when evaluated solely on images containing ships, by reducing false positives. Additionally, we calculated that evaluating the full validation set on a NVIDIA Jetson Nano device requires 111.4 kJ of energy. Our two-stage system reduces this energy consumption to 27.3 kJ, which is less than a fourth, demonstrating the efficiency of a heterogeneous computing system.