SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities

Morera, Ángel; Sánchez, Ángel Mediavilla; Moreno, Ana Belén; Sappa, Ángel D.; Vélez, José Francisco

doi:10.3390/s20164587

Cited by 59 publications

(30 citation statements)

References 31 publications

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“…Deep learning object detection algorithms are mostly used in natural scenes, such as traffic sign detection [23], vehicle detection [24], pedestrian detection [25], license plate recognition [26], and advertising panel recognition [27]. The acquisition of high-resolution satellite images makes deep learning gradually be applied in geoscience and remote sensing communities.…”

Section: Introductionmentioning

confidence: 99%

Oil Well Detection via Large-Scale and High-Resolution Remote Sensing Images Based on Improved YOLO v4

et al. 2021

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Oil is an important resource for the development of modern society. Accurate detection of oil wells is of great significance to the investigation of oil exploitation status and the formulation of an exploitation plan. However, detecting small objects in large-scale and high-resolution remote sensing images, such as oil wells, is a challenging task due to the problems of large number, limited pixels, and complex background. In order to overcome this problem, first, we create our own oil well dataset to conduct experiments given the lack of a public dataset. Second, we provide a comparative assessment of two state-of-the-art object detection algorithms, SSD and YOLO v4, for oil well detection in our image dataset. The results show that both of them have good performance, but YOLO v4 has better accuracy in oil well detection because of its better feature extraction capability for small objects. In view of the fact that small objects are currently difficult to be detected in large-scale and high-resolution remote sensing images, this article proposes an improved algorithm based on YOLO v4 with sliding slices and discarding edges. The algorithm effectively solves the problems of repeated detection and inaccurate positioning of oil well detection in large-scale and high-resolution remote sensing images, and the accuracy of detection result increases considerably. In summary, this study investigates an appropriate algorithm for oil well detection, improves the algorithm, and achieves an excellent effect on a large-scale and high-resolution satellite image. It provides a new idea for small objects detection in large-scale and high-resolution remote sensing images.

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Section: Introductionmentioning

confidence: 99%

Oil Well Detection via Large-Scale and High-Resolution Remote Sensing Images Based on Improved YOLO v4

et al. 2021

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“…It is usually used as the performance benchmark for model evaluation [ 60 ]. The object detection approaches take advantage of CNN-like technologies coupling with multi-region in multi-resolution parallel recognition capabilities and are popularly used image object detection methods [ 61 ]. These algorithms have been applied to many machine vision-related fields, such as the visual recognition of robots and autopilot cars, the defect recognition of online products, and automatic recognition of security systems, etc.…”

Section: Field Deployment and Resultsmentioning

confidence: 99%

The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods

Lin

Lee

Chang

et al. 2021

Sensors

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Scour around bridge piers remains the leading cause of bridge failure induced in flood. Floods and torrential rains erode riverbeds and damage cross-river structures, causing bridge collapse and a severe threat to property and life. Reductions in bridge-safety capacity need to be monitored during flood periods to protect the traveling public. In the present study, a scour monitoring system designed with vibration-based arrayed sensors consisting of a combination of Internet of Things (IoT) and artificial intelligence (AI) is developed and implemented to obtain real-time scour depth measurements. These vibration-based micro-electro-mechanical systems (MEMS) sensors are packaged in a waterproof stainless steel ball within a rebar cage to resist a harsh environment in floods. The floodwater-level changes around the bridge pier are performed using real-time CCTV images by the Mask R-CNN deep learning model. The scour-depth evolution is simulated using the hydrodynamic model with the selected local scour formulas and the sediment transport equation. The laboratory and field measurement results demonstrated the success of the early warning system for monitoring the real-time bridge scour-depth evolution.

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“…So, single shot multibox detector (SSD) adds the concept of anchor from Faster R-CNN on the basis of YOLO and combines the features of different convolutional layers to make predictions. e main contribution of SSD is the multireference and multiresolution detection techniques, which significantly improve the detection accuracy of a one-stage detector, especially for some tiny objects [64]. Although the methods of the YOLO and SSD series do not have the extraction of region proposals and it becomes faster, they inevitably lose information and accuracy.…”

Section: Deep Learning-based Objectmentioning

confidence: 99%

Survey on Deep Learning-Based Marine Object Detection

Zhang

et al. 2021

Journal of Advanced Transportation

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We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed.

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SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities

Cited by 59 publications

References 31 publications

Oil Well Detection via Large-Scale and High-Resolution Remote Sensing Images Based on Improved YOLO v4

Oil Well Detection via Large-Scale and High-Resolution Remote Sensing Images Based on Improved YOLO v4

The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods

Survey on Deep Learning-Based Marine Object Detection

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