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Augmenting a Pretrained Object Detection Model with Planar Pose Estimation Capability
AbstractThis paper presents a 2D pose estimation solution to the bin-picking problem for robotic gras** systems. By extending a pretrained object...
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Spatiotemporal tubelet feature aggregation and object linking for small object detection in videos
This paper addresses the problem of exploiting spatiotemporal information to improve small object detection precision in video. We propose a...
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Relative vectoring using dual object detection for autonomous aerial refueling
Once realized, autonomous aerial refueling will revolutionize unmanned aviation by removing current range and endurance limitations. Previous...
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A survey of model compression strategies for object detection
Deep neural networks (DNNs) have achieved great success in many object detection tasks. However, such DNNS-based large object detection models are...
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Regional filtering distillation for object detection
Knowledge distillation is a common and effective method in model compression, which trains a compact student model to mimic the capability of a large...
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A review of object detection: Datasets, performance evaluation, architecture, applications and current trends
Object detection is one of the most important and challenging branches of computer vision, whose main task is to classify and localize objects in...
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Granular computing based segmentation and textural analysis (GrCSTA) framework for object-based LULC classification of fused remote sensing images
Machine learning(ML) based techniques for Land Use Land Cover(LULC) classification is crucial for extracting valuable insights from satellite...
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EMSNet: Extremely multi-scale network for salient object detection
In salient object detection, accurately segmenting objects across scales and refining boundaries are crucial challenges. We introduce the Multi-UNet...
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Underwater object detection based on enhanced YOLOv4 architecture
Object detection and image restoration pose significant challenges in deep learning and computer vision. These tasks are widely employed in various...
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Image Retrieval Using Object Semantic Aggregation Histogram
Simulating primates’ ability to make fine visual discriminations for extracting visual features remains a challenge. To address this issue, a novel...
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Visual object tracking by using ranking loss and spatial–temporal features
This paper introduces a novel two-stream deep neural network tracker for robust object tracking. In the proposed network, we use both spatial and...
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Adaptive multi-object tracking algorithm based on split trajectory
Multi-object tracking (MOT) has wide-ranging applications in unmanned vehicles, military reconnaissance, and video surveillance. However, real-world...
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Object tracking using local structural information and energy minimization
Object tracking is one of the fundamental processes for many high level applications in the field of machine vision. Many challenges in this field...
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Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation
Weakly supervised object localization and semantic segmentation aim to localize objects using only image-level labels. Recently, a new paradigm has...
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X-Detect: explainable adversarial patch detection for object detectors in retail
Object detection models, which are widely used in various domains (such as retail), have been shown to be vulnerable to adversarial attacks. Existing...
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A systematic review of object detection from images using deep learning
The development of object detection has led to huge improvements in human interaction systems. Object detection is a challenging task because it...
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Instance-level Object relation module for one-stage Object Detection
Leveraging the contextual information at instance-level can improve the accuracy in object detection. However, the-state-of-the-art object detection...
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Refiner: a general object position refinement algorithm for visual tracking
Object tracking is an important topic in computer vision. Most existing trackers require an accurate initial position of the target. However, in the...
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Few-shot object detection via data augmentation and distribution calibration
General object detection has been widely developed and studied over the past few years, while few-shot object detection is still in the exploratory...
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A dataset for fire and smoke object detection
Fire and smoke object detection is of great significance due to the extreme destructive power of fire disasters. Most of the existing methods,...
