Object detection is one of the most challenging and fundamental topics in computer vision. Aiming to locate instances of objects in images, object detection has witnessed increasing attention in this decade. Recently, with the rapid development of machine learning and image processing technologies, object detection has achieved remarkable performance, leading to a wide range of applications, such as 3D object detection in autonomous driving and small object detection in remoting sense images. Many advanced machine learning algorithms (e.g., deep learning) and image processing technologies have been proposed to handle different cases of object detection, but there are still several main intractable problems when applying object detection in the real world—for example, how to detect unknown/untrained classes, how to train using limited annotations or unbalanced data distribution, how to process images related to small objects, how to achieve better-quality measurements, how to achieve effective detection for 3D objects, etc.

This Special Issue seeks original contributions of pioneer researchers addressing the abovementioned key problems and issues. We will only accept submissions related to machine learning and image processing for object detection. The topics of interest include (but are not limited to):

New machine learning algorithms for object detection;
Quality measurement for object detection;
New deep neural networks for object detection;
Open-world object detection;
Small object detection;
3D object detection;
Long-tailed object detection;
Object detection on drone imagery;
Image augmentation or pre-processing for object detection;
Post-processing approaches for object detection.

Guest Editors
Prof. Dr. Weifeng Liu
Dr. Igor García Olaizola
Dr. Bingfeng Zhang

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