TY - GEN
T1 - Small Object Detection Based on SSD-ResNeXt101
AU - Khusni, Uus
AU - Arymurthy, Aniati Murni
AU - Susanto, Heru
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Object detection is closely related to video analysis and image retention, which has attracted many researchers to research in this field. Traditional object detection methods are built with hand-crafted features and shallow trainable architectures. The resulting accuracy of traditional object detection is very much influenced by the selected features. The development of the field of Artificial Intelligence (AI), especially Deep Learning (DL), has made DL a powerful model for object detection. This is because DL has semantic analysis capabilities, high-level, deeper features, which are problems that often arise in traditional object detection. However, there are a few things that need to be fixed regarding the application of object detection in the real world because there are many small objects and varied backgrounds. Manual labeling of small objects is quite a time consuming and costly. The lack of a dataset to train small objects greatly affects the accuracy of the Convolutional Neural Network (CNN) model that was built. Single Shot Multi box Detector (SDD) as an object detection framework can detect objects of different sizes. To improve SSD accuracy in detecting small objects, in this paper, we replaced the SSD backbone using ResNeXt101. The experimental results yield better accuracy than the previous SSD framework with ResNet101. SSD (ResNeXt101) reach accurate 67.17% while SSD (ResNet101) with accurate 66.09%.
AB - Object detection is closely related to video analysis and image retention, which has attracted many researchers to research in this field. Traditional object detection methods are built with hand-crafted features and shallow trainable architectures. The resulting accuracy of traditional object detection is very much influenced by the selected features. The development of the field of Artificial Intelligence (AI), especially Deep Learning (DL), has made DL a powerful model for object detection. This is because DL has semantic analysis capabilities, high-level, deeper features, which are problems that often arise in traditional object detection. However, there are a few things that need to be fixed regarding the application of object detection in the real world because there are many small objects and varied backgrounds. Manual labeling of small objects is quite a time consuming and costly. The lack of a dataset to train small objects greatly affects the accuracy of the Convolutional Neural Network (CNN) model that was built. Single Shot Multi box Detector (SDD) as an object detection framework can detect objects of different sizes. To improve SSD accuracy in detecting small objects, in this paper, we replaced the SSD backbone using ResNeXt101. The experimental results yield better accuracy than the previous SSD framework with ResNet101. SSD (ResNeXt101) reach accurate 67.17% while SSD (ResNet101) with accurate 66.09%.
KW - CNN
KW - Deep Learning
KW - ResNet101
KW - ResNeXt101
KW - SSD
UR - http://www.scopus.com/inward/record.url?scp=85125254025&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-8129-5_162
DO - 10.1007/978-981-16-8129-5_162
M3 - Conference contribution
AN - SCOPUS:85125254025
SN - 9789811681288
T3 - Lecture Notes in Electrical Engineering
SP - 1058
EP - 1064
BT - Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications - Enhancing Research and Innovation through the Fourth Industrial Revolution
A2 - Mahyuddin, Nor Muzlifah
A2 - Mat Noor, Nor Rizuan
A2 - Mat Sakim, Harsa Amylia
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021
Y2 - 5 April 2021 through 6 April 2021
ER -