Dim Target Object Detection Using Deep Learning


Weldekiros Misgana Desalegne
Rui Qing Wu


With significant advancements in computer vision technology using deep learning algorithms, object detection has shown terrific performance in bright and clear targets. However, due to the low signal to noise ratio (SNR), the challenges of detec ting and segmenting objects in dim and dark targets continue to affect computer vision applications in dark and visually polluted conditions. Dim object detection using infrared imaging based on a deep learning algorithm, Convolutional Neural Network(CNN) for IR images, is
proposed to counter this challenge. By studying and analyzing a series of CNN algorithms, the author presents an application based on Mask R CNN that is better in precision and speed to detect and segment target objects in infrared images dim and dark targets.


How to Cite
Weldekiros Misgana Desalegne, & Rui Qing Wu. (2022). Dim Target Object Detection Using Deep Learning. IIRJET, 7(3). https://doi.org/10.32595/iirjet.org/v7i3.2022.153