Research Article
Forest Farm Fire Drone Monitoring System Based on Deep Learning and Unmanned Aerial Vehicle Imagery
| (1) | Load Image Img with a variable size PxQxR | | | ai, aj, ak: Color Greatness Img = ∑a(i, j, k) | | | where | | | i: 1 to P | | | j: 1 to Q | | | k: 1 to R | | (2) | Convert Gray Color Code of an input image | | | Img: Igc | | | where | | | ai, j: gray code balance Igc = ∑a(i, j) | | | i: 1 to P | | | j: 1 to Q | | (3) | Image Augmentation | | | Image enlargement and renovation: Image size is 3 × 3 | | | where | | | Regions obtained in Step 2 | | (4) | Pixel Edge Finding | | (5) | Text Area Finding | | | Tr = Igc | | | S ⊂ Igc | | (6) | MSER Detection Detect Extremal Regions | | | S ⊂ Tr ⊂ for all x ∈ S, y ∈ ∂T | | | where ∂T is the external area edge. | | (7) | Image Edge Finding | | | Level the image using an image filter | | | Find strength pitch of the image | | | Remove low-strength pixels | | | Apply threshold to discover the limits | | | Eliminate feeble limits and focus on the substantial limits |
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