Abstract
There have been several improvement in object detection and semantic segmentation results in recent years. Baseline systems that drives these advances are Fast/Faster R-CNN, Fully Convolutional Network and recently Mask R-CNN and its variant that has a weight transfer function. Mask R-CNN is the state-of-art. This research extends the application of the state-of-art in object detection and semantic segmentation in drone based datasets. Existing drone datasets was used to learn semantic segmentation on drone images using Mask R-CNN. This work is the result of my own activity. I have neither given nor received unauthorized assistance on this work.
Francis, M (2021). Fast and Accurate Feature-based Region Identification. Afribary. Retrieved from https://afribary.com/works/fast-and-accurate-feature-based-region-identification
Francis, Maduakor "Fast and Accurate Feature-based Region Identification" Afribary. Afribary, 13 Apr. 2021, https://afribary.com/works/fast-and-accurate-feature-based-region-identification. Accessed 06 May. 2024.
Francis, Maduakor . "Fast and Accurate Feature-based Region Identification". Afribary, Afribary, 13 Apr. 2021. Web. 06 May. 2024. < https://afribary.com/works/fast-and-accurate-feature-based-region-identification >.
Francis, Maduakor . "Fast and Accurate Feature-based Region Identification" Afribary (2021). Accessed May 06, 2024. https://afribary.com/works/fast-and-accurate-feature-based-region-identification