Traffic congestion-aware graph-based vehicle rerouting framework from aerial imagery

Bayraktar, Ertuğrul; Korkmaz, Burla Nur; Erarslan, Aras Umut; Çelebi, Numan

doi:10.1016/j.engappai.2022.105769

Cited by 14 publications

(5 citation statements)

References 82 publications

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“…Though the deep learning based methods showed powerful abilities in object detection, it was a certain waste of computing resources for smaller target detection by simply adopting general frameworks. Excessively complex models will be detrimental to deployments on terminals, such as applications in small drones, cars, robots, or other similar scenes [28] [29]. For lightweight model building, besides efficient structures like depthwise separable or group convolution in some mobile networks [30] [31], it was common to clip the unnecessary branches [32] [33] [12] or reuse some backbones or modules [34] to make the models efficient.…”

Section: B Lightweight Framework For Detectionmentioning

confidence: 99%

Real-Time Infrared Small Target Detection With Nonlocal Spatial-Temporal Feature Fusion

Xu,

Zhong,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: B Lightweight Framework For Detectionmentioning

confidence: 99%

Real-Time Infrared Small Target Detection With Nonlocal Spatial-Temporal Feature Fusion

Xu,

Zhong,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…We evaluate the detection performance using commonly used evaluation metrics in object detection and few-shot object detection, namely AP50 and mAP (Mean Average Precision). AP50 represents the average precision calculated at an IOU threshold of 50%, as shown in Equation (7).…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

“…Few-shot learning is a method to solve a problem with few training samples in the target domain, which can alleviate problems such as overfitting and the inability to converge deep-learning models in the case of few samples [5] and mainly contains metric learning, data augmentation, fine-tuning, meta-learning, and other methods [6]. Among them, data augmentation is the most commonly used technology for dealing with few-shot problems; for example, Bayraktar et al [7] proposed a method to optimize the route of the target ground vehicle benefit from some geometric transformations, effectively expanding the sample and enhancing the segmentation ability of the model. In addition, [8] presents a method to improve the performance of surface defect detection by combining GAN (Generative Adversarial Networks) [9] with classical methods.…”

Section: Introductionmentioning

confidence: 99%

A Few-Shot Defect Detection Method for Transmission Lines Based on Meta-Attention and Feature Reconstruction

Shi

Wang²,

Chao

et al. 2023

Applied Sciences

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In tasks of transmission line defect detection, traditional object detection algorithms are ineffective, with few training samples of defective components. Meta-learning uses multi-task learning as well as fine-tuning to learn common features in different tasks, which has the ability to adapt to new tasks quickly, shows good performance in few-shot object detection, and has good generalization in new tasks. For this reason, we proposed a few-shot defect detection method (Meta PowerNet) with a Meta-attention RPN and Feature Reconstruction Module for transmission lines based on meta-learning. First, in the stage of region proposal, a new region proposal network (Meta-Attention Region Proposal Network, MA-RPN) is designed to fuse the support set features and the query set features to filter the noise in anchor boxes. In addition, it has the ability to focus on the subtle texture features of smaller-sized objects by fusing low-level features from the query set. Second, in the meta-feature construction stage, we designed a meta-learner with the defect feature reconstruction module as the core to capture and focus on the defect-related feature channels. The experimental results show that under the condition, there are only 30 training objects for various types of component defects. The method achieves 72.5% detection accuracy for component defects, which is a significant improvement compared with other mainstream few-shot object detection. Meanwhile, the MA-RPN designed in this paper can be used in other meta-learning object detection models universally.

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“…Compared with other oriented object detectors, our PETDet can achieve state-of-the-art performance with competitive speed. cessful applications in remote sensing [4], [5], [6], it no longer meets the new demand for fine-grained recognition. In recent times, FGOD in aerial images has garnered widespread attention from the research community [7], [8], [9], [10], [11], [12], [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

HTD: Heterogeneous Task Decoupling for Two-Stage Object Detection

Chen

Li³

et al. 2021

IEEE Trans. on Image Process.

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Fine-grained object detection (FGOD) extends object detection with the capability of fine-grained recognition. In recent two-stage FGOD methods, the region proposal serves as a crucial link between detection and fine-grained recognition. However, current methods overlook that some proposalrelated procedures inherited from general detection are not equally suitable for FGOD, limiting the multi-task learning from generation, representation, to utilization. In this paper, we present PETDet (Proposal Enhancement for Two-stage finegrained object detection) to better handle the sub-tasks in twostage FGOD methods. Firstly, an anchor-free Quality Oriented Proposal Network (QOPN) is proposed with dynamic label assignment and attention-based decomposition to generate highquality oriented proposals. Additionally, we present a Bilinear Channel Fusion Network (BCFN) to extract independent and discriminative features of the proposals. Furthermore, we design a novel Adaptive Recognition Loss (ARL) which offers guidance for the R-CNN head to focus on high-quality proposals. Extensive experiments validate the effectiveness of PETDet. Quantitative analysis reveals that PETDet with ResNet50 reaches state-of-theart performance on various FGOD datasets, including FAIR1M-v1.0 (42.96 AP), FAIR1M-v2.0 (48.81 AP), MAR20 (85.91 AP) and ShipRSImageNet (74.90 AP). The proposed method also achieves superior compatibility between accuracy and inference speed. Our code and models will be released at https://github.com/ canoe-Z/PETDet.

show abstract

Traffic congestion-aware graph-based vehicle rerouting framework from aerial imagery

Cited by 14 publications

References 82 publications

Real-Time Infrared Small Target Detection With Nonlocal Spatial-Temporal Feature Fusion

Real-Time Infrared Small Target Detection With Nonlocal Spatial-Temporal Feature Fusion

A Few-Shot Defect Detection Method for Transmission Lines Based on Meta-Attention and Feature Reconstruction

HTD: Heterogeneous Task Decoupling for Two-Stage Object Detection

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