Drone vision is a surging area of research, primarily due to its surveillance and military uses.A camera-equipped drone is capable of carrying out a variety of operations like imagedetection, recognition, and classification. Image processing is an important part of theprocess; it is used in denoising and smoothing the image before recognition.We aimed to classify different images and command the drone to carry out various tasksdepending on the image shown. If shown a certain image, the drone would take off and landrespectively.We use the Euclidean distance algorithm to calculate the distance between two images. If thedistance equals zero, the images are equal. While the ideal result of 0 is impossible due tonoise, we can use digital image processing methods to reduce noise.We were able to classify basic images to some degree of accuracy; the drone was able tocarry out given tasks after a successful image classification.While Euclidean distance might be the first choice for most image-classification algorithms,it has many limitations. This might call for the use of other image processing algorithms toachieve better results.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:bth-24209 |
| Date | January 2022 |
| Creators | Gangavarapu, Mohith, Pawar, Arjun |
| Publisher | Blekinge Tekniska Högskola |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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