# ICanC: Improving Camera-based Object Detection and Energy Consumption in Low-Illumination Environments

Source: [ICanC: Improving Camera-based Object Detection and Energy Consumption in Low-Illumination Environments](<https://www.sota2.com/research/paper/68fa77431e209131cea72174>)

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

Data updated: 2025-10-26T04:13:53.325Z

Authors: Daniel Ma, Ren Zhong, Weisong Shi

Published: 2025-03-02T03:19:24.000Z

## Abstract

This paper introduces ICanC (pronounced "I Can See"), a novel system designed to enhance object detection and optimize energy efficiency in autonomous vehicles (AVs) operating in low-illumination environments. By leveraging the complementary capabilities of LiDAR and camera sensors, ICanC improves detection accuracy under conditions where camera performance typically declines, while significantly reducing unnecessary headlight usage. This approach aligns with the broader objective of promoting sustainable transportation.   ICanC comprises three primary nodes: the Obstacle Detector, which processes LiDAR point cloud data to fit bounding boxes onto detected objects and estimate their position, velocity, and orientation; the Danger Detector, which evaluates potential threats using the information provided by the Obstacle Detector; and the Light Controller, which dynamically activates headlights to enhance camera visibility solely when a threat is detected.   Experiments conducted in physical and simulated environments demonstrate ICanC's robust performance, even in the presence of significant noise interference. The system consistently achieves high accuracy in camera-based object detection when headlights are engaged, while significantly reducing overall headlight energy consumption. These results position ICanC as a promising advancement in autonomous vehicle research, achieving a balance between energy efficiency and reliable object detection.

## 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/2503.00709>)

[PDF](<https://arxiv.org/pdf/2503.00709.pdf>)