Dear Colleagues,

A fundamental problem in computer vision is the task of object identification. It is essential to many applications, from robots and augmented reality to autonomous driving and surveillance. Accurately localizing and categorizing items inside intricate and varied settings is one of the main issues in object detection. Deep learning methods have significantly improved object detection in recent years, creating new research opportunities and revolutionizing the field. Deep learning-based methods that use convolutional neural networks (CNNs) to extract rich representations of objects from unprocessed picture data have successfully addressed this difficulty. These models have enhanced detection resilience and accuracy by capturing complex patterns.

The Special Issue covers a broad spectrum of study areas, emphasising the creation of innovative architectures, feature extraction strategies, and training approaches for deep learning-based object identification. Researchers have explored a range of architectural designs, including one-stage detectors and two-stage detectors, each with unique advantages and disadvantages concerning accuracy and speed. Furthermore, due to their potential to enhance object detection and localization, attention processes, including self-attention and spatial attention, have drawn interest.

This Special Issue aims to present a thorough summary of current developments and new directions in deep learning for object detection, compiling original research and review articles on recent advances, technologies, solutions, applications, and novel challenges in this field.

Potential topics include, but are not limited to, the following:

• Advancements in convolutional neural networks for object detection;
• One-stage vs. two-stage detectors;
• Role of transfer learning in object detection;
• Real-time object detection for autonomous vehicles;
• Object detection in aerial imagery;
• Deep learning approaches to object detection in medical imaging;
• Challenges in dataset annotation and bias in object detection models;
• Edge computing and mobile deployment of object detection models.

Dr. António Manuel Trigueiros Da Silva Cunha
Dr. Sandra Pereira
Dr. Paulo Jorge Coelho
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• object detection
• deep learning
• instance segmentation
• architecture design
• feature extraction