Comparison of YOLO Versions for Object Detection from Aerial Images

Construction activities typically create large amounts of ground disturbance, which can lead to increased rates of soil erosion. Construction stormwater practices are used on active jobsites to protect downstream waterbodies from offsite sediment transport. Federal and state regulations require routine pollution prevention inspections to ensure that temporary stormwater practices are in place and performing as intended. This study addresses the existing challenges and limitations in the construction stormwater inspections and presents a unique approach for performing unmanned aerial system (UAS)-based inspections. Deep learning-based object detection principles were applied to identify and locate practices installed on active construction sites. The system integrates a post-processing stage by clustering results. The developed framework consists of data preparation with aerial inspections, model training, validation of the model, and testing for accuracy. The developed model was created from 800 aerial images and was used to detect four different types of construction stormwater practices at 100% accuracy on the Mean Average Precision (MAP) with minimal false positive detections. Results indicate that object detection could be implemented on UAS-acquired imagery as a novel approach to construction stormwater inspections and provide accurate results for site plan comparisons by rapidly detecting the quantity and location of field-installed stormwater practices.

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Improved object detection algorithms for optical aerial images based on region proposal network

10.1117/12.2604541 ◽

2021 ◽

Author(s):

Yuxing Lin ◽

Liaoni Wu ◽

Bo Huang ◽

Boyong He

Keyword(s):

Object Detection ◽

Aerial Images ◽

Detection Algorithms

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Object Detection Using Clustering Algorithm Adaptive Searching Regions in Aerial Images

Computer Vision – ECCV 2020 Workshops - Lecture Notes in Computer Science ◽

10.1007/978-3-030-66823-5_39 ◽

2020 ◽

pp. 651-664

Author(s):

Yi Wang ◽

Youlong Yang ◽

Xi Zhao

Keyword(s):

Object Detection ◽

Clustering Algorithm ◽

Aerial Images

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Analysis Of High Resolution Aerial Images For Object Detection

10.1117/12.960453 ◽

1989 ◽

Author(s):

Mohan M. Trivedi ◽

Amol G. Bokil ◽

Mourad B. Takla ◽

George B. Maksymonko ◽

J. Thomas Broach

Keyword(s):

High Resolution ◽

Object Detection ◽

Aerial Images

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A Comprehensive Review Towards Appropriate Feature Selection for Moving Object Detection Using Aerial Images

Advances in Visual Informatics - Lecture Notes in Computer Science ◽

10.1007/978-3-030-34032-2_21 ◽

2019 ◽

pp. 227-236

Author(s):

Zainal Rasyid Mahayuddin ◽

A. F. M. Saifuddin Saif

Keyword(s):

Feature Selection ◽

Object Detection ◽

Moving Object Detection ◽

Moving Object ◽

Aerial Images ◽

Comprehensive Review ◽

Selection For

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Extended Feature Pyramid Network with Adaptive Scale Training Strategy and Anchors for Object Detection in Aerial Images

Remote Sensing ◽

10.3390/rs12050784 ◽

2020 ◽

Vol 12 (5) ◽

pp. 784 ◽

Cited By ~ 1

Author(s):

Wei Guo ◽

Weihong Li ◽

Weiguo Gong ◽

Jinkai Cui

Keyword(s):

Neural Network ◽

Object Detection ◽

Semantic Information ◽

Aerial Images ◽

Training Strategy ◽

The Public ◽

Multi Scale ◽

Shallow Layer ◽

The Neural Network ◽

Feature Pyramid

Multi-scale object detection is a basic challenge in computer vision. Although many advanced methods based on convolutional neural networks have succeeded in natural images, the progress in aerial images has been relatively slow mainly due to the considerably huge scale variations of objects and many densely distributed small objects. In this paper, considering that the semantic information of the small objects may be weakened or even disappear in the deeper layers of neural network, we propose a new detection framework called Extended Feature Pyramid Network (EFPN) for strengthening the information extraction ability of the neural network. In the EFPN, we first design the multi-branched dilated bottleneck (MBDB) module in the lateral connections to capture much more semantic information. Then, we further devise an attention pathway for better locating the objects. Finally, an augmented bottom-up pathway is conducted for making shallow layer information easier to spread and further improving performance. Moreover, we present an adaptive scale training strategy to enable the network to better recognize multi-scale objects. Meanwhile, we present a novel clustering method to achieve adaptive anchors and make the neural network better learn data features. Experiments on the public aerial datasets indicate that the presented method obtain state-of-the-art performance.

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Computerized or Automated Object Recognition and Analysis of Pattern Matching in Runways Using Surface Track Data

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/10867 ◽

2021 ◽

Vol 23 (11) ◽

pp. 159-165

Author(s):

JAYANTH DWIJESH H P ◽

◽

SANDEEP S V ◽

RASHMI S ◽

◽

...

Keyword(s):

Edge Detection ◽

Object Detection ◽

Pattern Matching ◽

Aerial Images ◽

3 Dimensional ◽

Detection Techniques ◽

Detection Algorithms ◽

Detection Analysis ◽

Efficient Detection ◽

Minimal Damage

In today’s world, accurate and fast information is vital for safe aircraft landings. The purpose of an EMAS (Engineered Materials Arresting System) is to prevent an aeroplane from overrunning with no human injury and minimal damage to the aircraft. Although various algorithms for object detection analysis have been developed, only a few researchers have examined image analysis as a landing assist. Image intensity edges are employed in one system to detect the sides of a runway in an image sequence, allowing the runway’s 3-dimensional position and orientation to be approximated. A fuzzy network system is used to improve object detection and extraction from aerial images. In another system, multi-scale, multiplatform imagery is used to combine physiologically and geometrically inspired algorithms for recognizing objects from hyper spectral and/or multispectral (HS/MS) imagery. However, the similarity in the top view of runways, buildings, highways, and other objects is a disadvantage of these methods. We propose a new method for detecting and tracking the runway based on pattern matching and texture analysis of digital images captured by aircraft cameras. Edge detection techniques are used to recognize runways from aerial images. The edge detection algorithms employed in this paper are the Hough Transform, Canny Filter, and Sobel Filter algorithms, which result in efficient detection.

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