车辆的快速、精确检测在智能交通系统中具有重要作用。当前车辆检测算法面临计算复杂度高、检测率低以及在移动设备上可行性有限等挑战。为解决这些问题，本文提出一种基于YOLOv7-tiny（You Only Look Once version seven）的轻量化车辆检测算法，称为Ghost-YOLOv7。将模型宽度缩放至0.5，并将骨干网络的标准卷积替换为Ghost卷积，以实现更轻量的网络并提高检测速度；随后在颈部网络中嵌入一种自设计的Ghost双向特征金字塔网络（Ghost-BiFPN），以增强算法的特征提取能力并丰富语义信息；并采用Ghost解耦头（GDH）对车辆位置和类别进行准确预测；最后，在输出层引入坐标注意力机制以抑制环境干扰。采用WIoU损失函数进一步提升检测精度。在PASCAL VOC数据集上的消融实验结果表明，Ghost-YOLOv7优于原始YOLOv7-tiny模型。其计算量减少29.8%，参数量减少37.3%，模型权重减少35.1%，平均精度均值（mAP）提高1.1%，检测速度相比原始算法提高27FPS。Ghost-YOLOv7还在KITTI和BIT-vehicle数据集上进行了比较，结果表明该算法具有整体最佳性能。
英文摘要：
The swift and precise detection of vehicles plays a significant role in intelligent transportation systems. Current vehicle detection algorithms encounter challenges of high computational complexity, low detection rate, and limited feasibility on mobile devices. To address these issues, this paper proposes a lightweight vehicle detection algorithm based on YOLOv7-tiny (You Only Look Once version seven) called Ghost-YOLOv7. The width of model is scaled to 0.5 and the standard convolution of the backbone network is replaced with Ghost convolution to achieve a lighter network and improve the detection speed; then a self-designed Ghost bi-directional feature pyramid network (Ghost-BiFPN) is embedded into the neck network to enhance feature extraction capability of the algorithm and enriches semantic information; and a Ghost Decouoled Head (GDH) is employed for accurate prediction of vehicle location and species; finally, a coordinate attention mechanism is introduced into the output layer to suppress environmental interference. The WIoU loss function is employed to further enhance the detection accuracy. Ablation experiments results on the PASCAL VOC dataset demonstrate that Ghost-YOLOv7 outperforms the original YOLOv7-tiny model. It achieving a 29.8% reduction in computation, 37.3% reduction in the number of parameters, 35.1% reduction in model weights, 1.1% higher mean average precision (mAP), the detection speed is higher 27FPS compared with the original algorithm. Ghost-YOLOv7 was also compared on KITTI and BIT-vehicle datasets as well, and the results show that this algorithm has the overall best performance.
In recent years, the satellite videos have been captured by a moving satellite platform. In contrast to consumer, movie, and common surveillance videos, satellite video can record the snapshot of the city-scale scene. In a broad field-of-view of satellite videos, each moving target would be very tiny and usually composed of several pixels in frames. Even worse, the noise signals also existed in the video frames, since the background of the video frame has the subpixel-level and uneven moving thanks to the motion of satellites. We argue that this is a new type of computer vision task since prev…
Object detection has made great progress in the past few years along with the development of deep learning. However, most current object detection methods are resource hungry, which hinders their wide deployment to many resource restricted usages such as usages on always-on devices, battery-powered low-end devices, etc. This paper considers the resource and accuracy trade-off for resource-restricted usages during designing the whole object detection framework. Based on the deeply supervised object detection (DSOD) framework, we propose Tiny-DSOD dedicating to resource-restricted usages. Tiny-D…
Recent works on convolutional neural networks (CNNs) for facial alignment have demonstrated unprecedented accuracy on a variety of large, publicly available datasets. However, the developed models are often both cumbersome and computationally expensive, and are not adapted to applications on resource restricted devices. In this work, we look into developing and training compact facial alignment models that feature fast inference speed and small deployment size, making them suitable for applications on the aforementioned category of devices. Our main contribution lies in designing such small mo…
T3DNet: Compressing Point Cloud Models for Lightweight 3D Recognition
School of Electrical and Electronics Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）
Moscow Institute for Physics and Technology（莫斯科物理技术学院）；Institute for Systems Analysis, Federal Research Center “Computer Science and Control” of Russian Academy of Sciences（俄罗斯科学院计算机科学与控制联邦研究中心系统分析研究所）；Smart Engines Service LLC（Smart Engines Service有限责任公司）；National University of Science and Technology “MISIS”（国家研究型技术大学“MISIS”）
In this paper, we study the problem of feature points description in the context of document analysis and template matching. Our study shows that the specific training data is required for the task especially if we are to train a lightweight neural network that will be usable on devices with limited computational resources. In this paper, we construct and provide a dataset with a method of training patches retrieval. We prove the effectiveness of this data by training a lightweight neural network and show how it performs in both documents and general patches matching. The training was done on …
Multi-scale learning frameworks have been regarded as a capable class of models to boost semantic segmentation. The problem nevertheless is not trivial especially for the real-world deployments, which often demand high efficiency in inference latency. In this paper, we thoroughly analyze the design of convolutional blocks (the type of convolutions and the number of channels in convolutions), and the ways of interactions across multiple scales, all from lightweight standpoint for semantic segmentation. With such in-depth comparisons, we conclude three principles, and accordingly devise Lightwei…
National Central University（中央大学）；National Chiao Tung University（交通大学）
The development of lightweight object detectors is essential due to the limited computation resources. To reduce the computation cost, how to generate redundant features plays a significant role. This paper proposes a new lightweight Convolution method Cross-Stage Lightweight (CSL) Module, to generate redundant features from cheap operations. In the intermediate expansion stage, we replaced Pointwise Convolution with Depthwise Convolution to produce candidate features. The proposed CSL-Module can reduce the computation cost significantly. Experiments conducted at MS-COCO show that the proposed…
Indian Institute of Technology Bhubaneswar（布巴内斯瓦尔印度理工学院）
Detection and classification of vehicles are very significant components in an Intelligent-Transportation System. Existing solutions not only use heavy-weight and costly equipment, but also largely depend on constant cloud (Internet) connectivity, as well as adequate uninterrupted power-supply. Such dependencies make these solutions fundamentally impractical considering the possible adversities of outdoor environment as well as requirement of correlated wide-area operation. For practical use, apart from being technically sound and accurate, a solution has to be lightweight, cost-effective, eas…
With the increasing demand for search and rescue, it is highly demanded to detect objects of interest in large-scale images captured by Unmanned Aerial Vehicles (UAVs), which is quite challenging due to extremely small scales of objects. Most existing methods employed Feature Pyramid Network (FPN) to enrich shallow layers' features by combing deep layers' contextual features. However, under the limitation of the inconsistency in gradient computation across different layers, the shallow layers in FPN are not fully exploited to detect tiny objects. In this paper, we propose a Scale Selection Pyr…
Vel Tech Rangarajan Dr. Sagunthala R &D Institute of Science and Technology（Vel Tech Rangarajan Dr. Sagunthala R&D科学技术研究院）
Vehicle detection in real-time is a challenging and important task. The existing real-time vehicle detection lacks accuracy and speed. Real-time systems must detect and locate vehicles during criminal activities like theft of vehicle and road traffic violations with high accuracy. Detection of vehicles in complex scenes with occlusion is also extremely difficult. In this study, a lightweight model of deep neural network LittleYOLO-SPP based on the YOLOv3-tiny network is proposed to detect vehicles effectively in real-time. The YOLOv3-tiny object detection network is improved by modifying its f…
Recently, the convolutional neural network has brought impressive improvements for object detection. However, detecting tiny objects in large-scale remote sensing images still remains challenging. First, the extreme large input size makes the existing object detection solutions too slow for practical use. Second, the massive and complex backgrounds cause serious false alarms. Moreover, the ultratiny objects increase the difficulty of accurate detection. To tackle these problems, we propose a unified and self-reinforced network called remote sensing region-based convolutional neural network ($\…
In this work, we present and evaluate a method to perform real-time multiple drone detection and three-dimensional localization using state-of-the-art tiny-YOLOv4 object detection algorithm and stereo triangulation. Our computer vision approach eliminates the need for computationally expensive stereo matching algorithms, thereby significantly reducing the memory footprint and making it deployable on embedded systems. Our drone detection system is highly modular (with support for various detection algorithms) and capable of identifying multiple drones in a system, with real-time detection accur…
ASUS Intelligent Cloud Services, Asustek Computer Inc.（华硕电脑股份有限公司智能云服务）；National Taiwan University（国立台湾大学）
Underwater image enhancement is an important low-level computer vision task for autonomous underwater vehicles and remotely operated vehicles to explore and understand the underwater environments. Recently, deep convolutional neural networks (CNNs) have been successfully used in many computer vision problems, and so does underwater image enhancement. There are many deep-learning-based methods with impressive performance for underwater image enhancement, but their memory and model parameter costs are hindrances in practical application. To address this issue, we propose a lightweight adaptive f…
DPNet: Dynamic Pooling Network for Tiny Object Detection
School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机科学学院）；Research Center for Space Computing System, Zhejiang Lab（浙江实验室空间计算系统研究中心）；SWJTU-LEEDS Joint School, Southwest Jiaotong University（西南交通大学SWJTU-LEEDS联合学院）；Guangdong Zhiyun City construction Technology Co., LTD（广东智云城市建设技术有限公司）；Department of Control Science and Engineering, College of Electronic and Information Engineering, Tongji University（同济大学控制科学与工程系）；Key Laboratory of Maritime Intelligent Network Information Technology, Ministry of Education, Hohai University（河海大学航海智能网络信息科技重点实验室）
COMSATS University Islamabad（COMSATS伊斯兰堡大学）；Zhejiang University（浙江大学）
Technological advancements have normalized the usage of unmanned aerial vehicles (UAVs) in every sector, spanning from military to commercial but they also pose serious security concerns due to their enhanced functionalities and easy access to private and highly secured areas. Several instances related to UAVs have raised security concerns, leading to UAV detection research studies. Visual techniques are widely adopted for UAV detection, but they perform poorly at night, in complex backgrounds, and in adverse weather conditions. Therefore, a robust night vision-based drone detection system is …
Small Dents, Big Impact: A Dataset and Deep Learning Approach for Vehicle Dent Detection
School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST)（电气工程与计算机科学学院 国家科学与技术大学）；Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano（电子信息与生物工程系 米兰理工学院）
University of Amsterdam（阿姆斯特丹大学）；The University of Tokyo（东京大学）
Detecting tiny objects in a high-resolution video is challenging because the visual information is little and unreliable. Specifically, the challenge includes very low resolution of the objects, MPEG artifacts due to compression and a large searching area with many hard negatives. Tracking is equally difficult because of the unreliable appearance, and the unreliable motion estimation. Luckily, we found that by combining this two challenging tasks together, there will be mutual benefits. Following the idea, in this paper, we present a neural network model called the Recurrent Correlational Netw…
In this research, we propose a tiny image segmentation model, L^3U-net, that works on low-resource edge devices in real-time. We introduce a data folding technique that reduces inference latency by leveraging the parallel convolutional layer processing capability of the CNN accelerators. We also deploy the proposed model to such a device, MAX78000, and the results show that L^3U-net achieves more than 90% accuracy over two different segmentation datasets with 10 fps.
Clova AI Research, NAVER Corp.（NAVER公司Clova人工智能研究院）
In this paper, we propose a new multi-scale face detector having an extremely tiny number of parameters (EXTD),less than 0.1 million, as well as achieving comparable performance to deep heavy detectors. While existing multi-scale face detectors extract feature maps with different scales from a single backbone network, our method generates the feature maps by iteratively reusing a shared lightweight and shallow backbone network. This iterative sharing of the backbone network significantly reduces the number of parameters, and also provides the abstract image semantics captured from the higher s…
SBP-YOLO:A Lightweight Real-Time Model for Detecting Speed Bumps and Potholes toward Intelligent Vehicle Suspension Systems
Key Laboratory for Optoelectronic Technology and Systems, Ministry of Education, College of Optoelectronic Engineering, Chongqing University（光电技术及系统重点实验室、教育部长春电子工程学院、重庆大学）
College of Computing, Yonsei University（延世大学计算学院）；CLOVA ImageVision, NAVER（NAVER CLOVA图像视觉部门）
This work is for designing one-stage lightweight detectors which perform well in terms of mAP and latency. With baseline models each of which targets on GPU and CPU respectively, various operations are applied instead of the main operations in backbone networks of baseline models. In addition to experiments about backbone networks and operations, several feature pyramid network (FPN) architectures are investigated. Benchmarks and proposed detectors are analyzed in terms of the number of parameters, Gflops, GPU latency, CPU latency and mAP, on MS COCO dataset which is a benchmark dataset in obj…
FLUID: A Fine-Grained Lightweight Urban Signalized-Intersection Dataset of Dense Conflict Trajectories
School of Intelligent Systems Engineering, Sun Yat-sen University（智能系统工程学院，中山大学）；Guangdong Provincial Key Laboratory of Intelligent Transportation Systems（广东省智能交通系统重点实验室）；Pengcheng Laboratory（鹏城实验室）
AIT Austrian Institute of Technology, Center for Digital Safety & Security（奥地利技术学院（AIT）数字安全与保障中心）；TU Wien, Institute of Visual Computing & Human-Centered Technology（维也纳技术大学视觉计算与以人类为中心技术研究所）
Vehicle detection in aerial and satellite images is still challenging due to their tiny appearance in pixels compared to the overall size of remote sensing imagery. Classical methods of object detection very often fail in this scenario due to violation of implicit assumptions made such as rich texture, small to moderate ratios between image size and object size. Satellite video is a very new modality which introduces temporal consistency as inductive bias. Approaches for vehicle detection in satellite video use either background subtraction, frame differencing or subspace methods showing moder…
Non-local (NL) block is a popular module that demonstrates the capability to model global contexts. However, NL block generally has heavy computation and memory costs, so it is impractical to apply the block to high-resolution feature maps. In this paper, to investigate the efficacy of NL block, we empirically analyze if the magnitude and direction of input feature vectors properly affect the attention between vectors. The results show the inefficacy of softmax operation which is generally used to normalize the attention map of the NL block. Attention maps normalized with softmax operation hig…
