Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions
Convolutional neural networks (CNNs) are one of the main types of neural networks used for
image recognition and classification. CNNs have several uses, some of which are object …
image recognition and classification. CNNs have several uses, some of which are object …
A survey and performance evaluation of deep learning methods for small object detection
In computer vision, significant advances have been made on object detection with the rapid
development of deep convolutional neural networks (CNN). This paper provides a …
development of deep convolutional neural networks (CNN). This paper provides a …
A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection
Object detection is a well-known task in the field of computer vision, especially the small
target detection problem that has aroused great academic attention. In order to improve the …
target detection problem that has aroused great academic attention. In order to improve the …
TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios
Object detection on drone-captured scenarios is a recent popular task. As drones always
navigate in different altitudes, the object scale varies violently, which burdens the …
navigate in different altitudes, the object scale varies violently, which burdens the …
Yolo-facev2: A scale and occlusion aware face detector
In recent years, face detection algorithms based on deep learning have made great
progress. Nevertheless, the effective utilization of face detectors for small and occlusion …
progress. Nevertheless, the effective utilization of face detectors for small and occlusion …
Simple copy-paste is a strong data augmentation method for instance segmentation
Building instance segmentation models that are data-efficient and can handle rare object
categories is an important challenge in computer vision. Leveraging data augmentations is a …
categories is an important challenge in computer vision. Leveraging data augmentations is a …
A comparative analysis of object detection metrics with a companion open-source toolkit
Recent outstanding results of supervised object detection in competitions and challenges
are often associated with specific metrics and datasets. The evaluation of such methods …
are often associated with specific metrics and datasets. The evaluation of such methods …
Rethinking counting and localization in crowds: A purely point-based framework
Localizing individuals in crowds is more in accordance with the practical demands of
subsequent high-level crowd analysis tasks than simply counting. However, existing …
subsequent high-level crowd analysis tasks than simply counting. However, existing …
Varifocalnet: An iou-aware dense object detector
Accurately ranking the vast number of candidate detections is crucial for dense object
detectors to achieve high performance. Prior work uses the classification score or a …
detectors to achieve high performance. Prior work uses the classification score or a …
Align deep features for oriented object detection
The past decade has witnessed significant progress on detecting objects in aerial images
that are often distributed with large-scale variations and arbitrary orientations. However …
that are often distributed with large-scale variations and arbitrary orientations. However …