High Precision Single Shot Object Detection in Automotive Scenarios

SOUMYA A, C Krishna Mohan

Abstract:

Object detection in low-light scenarios is a challenging task with numerous real-world applications, ranging
from surveillance and autonomous vehicles to augmented reality. However, due to reduced visibility and
limited information in the image data, carrying out object detection in low-lighting settings brings distinct
challenges. This paper introduces a novel object detection model designed to excel in low-light imaging conditions, prioritizing inference speed and accuracy. The model leverages advanced deep-learning techniques
and is optimized for efficient inference on resource-constrained devices. The inclusion of cross-stage partial (CSP) connections is key to its effectiveness, which maintains low computational complexity, resulting
in minimal training time. This model adapts seamlessly to low-light conditions through specialized feature
extraction modules, making it a valuable resource in challenging visual environments.