Authors:
Soumya A
1
;
C Krishna Mohan
1
and
Linga Reddy Cenkeramaddi
2
Affiliations:
1
Department of Computer Science and Engineering, Indian Institute of Technology, Hyderabad, India
;
2
Department of Information and Communication Technology, University of Agder, Grimstad, 4879, Norway
Keyword(s):
Deep Learning, Convolutional Neural Network, Object Detection, Multi-Class Classification, Computer Vision.
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.