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    A Comparative Study for 6D Pose Estimation of Textureless and Symmetric Objects Used in Automotive Manufacturing Industry
    (Institute of Electrical and Electronics Engineers Inc., 2023) Doruk, Abdullah Enes; Ozkaya, Tayyip Ensar; Gulmez, Furkan; Uslu, Fatmatulzehra
    6D pose estimation of industrial objects on RGB images has a high potential to accelerate the automation of robotic manipulations in the automotive manufacturing industry. Despite its high potential, this problem has not been adequately addressed in the computer vision community. Main factors leading to under investigation of this problem are industrial objects to be textureless, thin, and symmetrical, which hinder the automatic estimation of their poses from color images. Deep learning models have shown promising results for pose estimation of household objects thanks to availability of large datasets with labels. In contrast to many household objects, there are few datasets for industrial objects with limited representation capacity, which restricts the use of deep models in pose estimation of industrial objects. In this study, we examine the eligibility of deep models on 6D pose estimation of industrial objects used in the automotive manufacturing industry. For this aim, we compare the performance of three deep models, DeepIM, CosyPose, and EPOS. To meet the need for large training dataset of these models, we produce a large synthetic dataset from the CAD data of the industrial objects. We also collect a small real dataset for training and performance evaluation purposes. We find that CosyPose outperforms other methods with a large margin, by showing its potential to solve such a hard problem. We also observe that training models with both synthetic and real images yield the best results. © 2023 IEEE.
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    Saw-YOLOv5: Scale-Aware YOLOv5 for Object Detection in Aerial Images
    (Institute of Electrical and Electronics Engineers Inc., 2023) Doruk, Abdullah Enes; Algul, Mucteba; Akyurek, Feyzullah; Alpaydm, Osman Kursat; Uslu, Fatmatulzehra
    The detection of objects in aerial images is impor-tant for many real world problems related to military defense, transportation, and etc. However, this is a challenging task as a result of the presence of various scales of objects in the same image, the large variety of contexts across aerial images, various brightness levels due to image acquisition at different times of the day and so on. To address these challenges, this paper introduces Saw-YOLOv5 for object detection in aerial images. Saw-YOLOv5 is a deep network based on YOLOv5, which was proposed for object detection in natural images. Saw-YOLOv5 extends YOLOv5 with the addition of several attention modules in its design. The results of our experiments, conducted on the aerial dataset delivered by the Turkey Technology Team for the Artificial Intelligence in Transportation Competition, showed that Saw-YOLOv5 outperforms previous methods, particularly for pedestrian detection, by yielding a mean mAP of 80.23% over all objects. © 2023 IEEE.