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searchwing-bilderkennung [2018/11/24 14:28]
beckmanf dota eingefügt
searchwing-bilderkennung [2019/06/17 00:22] (current)
wf68spef
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-==== Bilderkennung ​====+====== Image-Processing ======
  
-Das Ziel der Bilderkennung ist die Klassifikation der aufgenommenen Bilder von der DrohneIst auf dem Bild nur Wasser oder ist dort irgendetwas anderesbeispielsweise ein Boot?+By analysing the image with we receive from the camera, we can detect boats on the seaTo achieve thisdifferent algorithms and approaches from the image processing and deep learning domain can be used.
  
-=== Social ​===+===== Detection by using edgedetector and a 2d tracker in world coordinates =====
  
-  * https://​searchwingcv.slack.com+{{:ros.jpg?​nolink&​1311x744|ros.jpg}}
  
-=== Bildbeispiele ​===+==== Code ====
  
-Für die Entwicklung einer Bilderkennung benötigen wir BeispielfotosUnsere Quellen bislang:+[[https://​gitlab.com/​searchwing/​development/​boatdetectorcpp|https://​gitlab.com/​searchwing/​development/​boatdetectorcpp]]
  
-  * [[searchwing-flug-20180328|Flug zum Bodensee am 28.2.2018]] +==== Algorithms ====
-  * Bilder vom [[http://​v.bayern.de/​plus|Landesamt für Digitalisierung,​ Breitband und Vermessung]] +
-  * Wir haben Zugriff auf den [[http://​vislab.isr.ist.utl.pt/​seagull-dataset/​|Seagull Dataset]] +
-  * [[https://​www.kaggle.com/​c/​airbus-ship-detection|Kaggle Airbus Ship Detection Competition]] +
-  * [[searchwing-flug-virus-20181007|Flug am 7.10.2018 mit der Pipistrel Virus zum Bodensee]] +
-  * [[https://​captain-whu.github.io/​DOTA/​|DOTA Airial Image Dataset (Wuhan Univ./​Cornell/​DLR)]]+
  
-=== Bilderkennung mit OpenCV Haar Cascade Classifier ​===+=== Assumptions for the algorithms ​===
  
-Ich habe einige Fotos und Code für die Erkennung zusammengestellt. Der Code ist hier:+  * By flying over the sea with 50-80km/h boats can be assumed to stand almost still in relation to the drone 
 +  * Waves appear and dissapear over time 
 +  * Approach to detect boats 
 +      * Detect parts in the image which dont change over time 
 +      * Redect parts by checking the same position in consecutive frames 
 +      * If parts look the same, save them 
 +      * If something is redetected over 3 frames, we can assume this could be a boat
  
-https://​git.etech.fh-augsburg.de/​friedrich.beckmann/​bilderkennung+=== Proposal ​boat detector ===
  
-Die Bilderkennung markiert ​in dem Bild mögliche Boote. Das sieht dann so aus:+  * Detection of randomly looking objects ​in the water 
 +      * Classical image processing ( no deep learning) 
 +      * Processing chain depicted in the image 
 +  * There false positives after inital detection 
 +  * Calculation of the 3D Position of the Objects 
 +      * In world coordinates 
 +      * By using a camera calibration 
 +  * Code optimized for ARM Platforms 
 +      * By using ARM related flags for OpenCV 
 +      * AlternativeCompute Library for even faster processing 
 +  * Runtime 
 +      * 0,8 sec @ 8 MegaPixel(3240×2480 Pixel) @ Raspberry Pi 3
  
-{{::​bilderkennung-beispiel.jpg|Bilderkennung Beispiel}} +{{:0cae1334da2eb44438be608612fe6628.png}}
  
-Und in der Vergrößerung so:+
 + 
 +=== Tracking === 
 + 
 +  * Redetect boats in consecutive frames 
 +      * Get more information/​images about the same boats 
 +        * See image below 
 +      * Reduce false-positive rate 
 +        * Valid detections only if we redetect the same boat 
 +      * Algorithms 
 +        * Association Problem 
 +          * Euclidean distance based cost matrix between each possible track and detection 
 +          * Solve global neigherest neighbor assignement problem by using Hungarian Algorithm 
 +        * Tracking 
 +          * Different trackingmodels possible 
 +          * Constant position 
 +          * Kalman constant position 
 +          * Kalman constant velocity 
 + 
 +{{:​5ee30e4d3dc8729b80b2ca22e4b9f3af.png}} 
 + 
 +=== Output === 
 + 
 +  * Save boat images on harddisk 
 +  * Metadata for each detection is saved in exif-datablock of each detected boat 
 +      * GPS Position 
 +      * GPS Time 
 +  * Visualization of the detection in digikam 
 + 
 +{{:​digikamdetections.png?​nolink&​878x498|asd}} 
 + 
 +
 + 
 +==== Used software ==== 
 + 
 +  * ROS 
 +      * Module communication 
 +      * Coordinatesystem transformations 
 +      * Recording and playback of datasets 
 +      * Visualisation 
 +  * MAVROS 
 +      * Communication with the drone via MAVLINK 
 +  * OpenCV 
 +      * Imageprocessing 
 +  * ARM Compute Library 
 +      * Imageprocessing 
 + 
 +==== Outlook ==== 
 + 
 +  * Deep Learning 
 +      * Test different approaches 
 +      * Runtime evaluation for embedded hardware 
 +  * Interfaces to send detections via MAVLINK to basestation 
 + 
 +==== Presentation on the topic ==== 
 + 
 +[[https://​media.freifunk.net/​v/​35c3oio-77-detection-of-refugee-boats-on-the-mediterranean-with-a-drone-by-using-foss|Click to go to the video]] 
 + 
 +===== Imagerecognition by using a OpenCV Haar Cascade Classifier ===== 
 + 
 +I composed a few images to train a Haar Cascade Classifier. The code is over here: 
 + 
 +[[https://​git.etech.fh-augsburg.de/​friedrich.beckmann/​bilderkennung|https://​git.etech.fh-augsburg.de/​friedrich.beckmann/​bilderkennung]] 
 + 
 +The detected boats can be seen in the following image. They are marked by a blue rectangle. 
 + 
 +{{:​bilderkennung-beispiel.jpg?​1101x1044|Bilderkennung Beispiel}} 
 + 
 +Recified: 
 + 
 +{{:​bilderkennung-beispiel-detail.jpg?​1082x1027|Bilderkennung Beispiel Detail}} 
 + 
 +In this example the second boat with Persenning does not get detect. The boat above get detected. 
 + 
 +===== Datasets ===== 
 + 
 +For development and test purposes we need datasets. So far we got these datasets: 
 + 
 +  * [[:​searchwing-flug-20180328|Flug zum Bodensee am 28.2.2018]] 
 +  * [[http://​v.bayern.de/​plus|Landesamt für Digitalisierung,​ Breitband und Vermessung]] 
 +  * We got access to the [[http://​vislab.isr.ist.utl.pt/​seagull-dataset/​|Seagull Dataset]] 
 +  * [[https://​www.kaggle.com/​c/​airbus-ship-detection|Kaggle Airbus Ship Detection Competition]] 
 +  * [[:​searchwing-flug-virus-20181007|Flug am 7.10.2018 mit der Pipistrel Virus zum Bodensee]] 
 +  * [[https://​captain-whu.github.io/​DOTA/​|DOTA Airial Image Dataset (Wuhan Univ./​Cornell/​DLR)]]
  
-{{::​bilderkennung-beispiel-detail.jpg|Bilderkennung Beispiel Detail}} 
  
-In diesem Beispiel wird das zweite Boot mit der blauen Persenning nicht erkannt. Das Boot darüber ist mit dem blauen Rechteck markiert. 
  • searchwing-bilderkennung.1543066132.txt.gz
  • Last modified: 2018/11/24 14:28
  • by beckmanf