Semantic Segmentation of Endangered Tree Species in Brazilian Savanna Using Deeplabv3+ Variants

Abstract

Abstract. Knowing the spatial distribution of endangered tree species in a forest ecosystem or forest remnants is a valuable information to support environmental conservation practices. The use of Unmanned Aerial Vehicles (UAVs) offers a suitable alternative for this task, providing very high-resolution images at low costs. In parallel, recent advances in the computer vision field have led to the development of effective deep learning techniques for end-to-end semantic image segmentation. In this scenario, the DeepLabv3+ is well established as the state-of-the-art deep learning method for semantic segmentation tasks. The present paper proposes and assesses the use of DeepLabv3+ for mapping the threatened Dipteryx alata Vogel tree, popularly also known as cumbaru. We also compare two backbone networks for feature extraction in the DeepLabv3+ architecture: the Xception and MobileNetv2. Experiments carried out on a dataset consisting of 225 UAV/RGB images of an urban area in Midwest Brazil demonstrated that DeepLabv3+ was able to achieve in mean overall accuracy and F1-score above 90%, and IoU above 80%. The experimental analysis also pointed out that the MobileNetv2 backbone overcame its counterpart by a wide margin due to its comparatively simpler architecture in view of the available training data.

Detection of Cherry Tree Crown Based on Improved LA-dpv3+ Algorithm

“…Three variants of DeepLabv3+ were tested with ResNet, Xception, and MobileNetv2 backbones. The results align with those specified in Torres et al [30], demonstrating that the MobileNetv2 variant consistently outperformed its Xception counterpart. The performance was further improved by combining MobileNetv2 with the enhanced CBAM.…”

“…In contrast, our approach in this paper emphasizes the use of semantic segmentation. Semantic segmentation algorithms excel at capturing object boundaries compared to object detection and offer computational efficiency compared to instance segmentation [30].…”

“…For example, although it improves accuracy and speed by adopting the improved Xception backbone, these improvements come at the price of complex architectures and persistently high computational costs, which make it difficult to run on farm terminals with limited computing power. Hence, Torres evaluated the performance of Xception and MobileNetv2 as interchangeable backbone models of DeepLabv3+ for Brazilian Cumbaru tree segmentation [30].…”

Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera

“…In particular, two state-ofthe-art segmentation networks were employed in this work, which are SegNet and a recent segmentation network known as DeepLabv3+ networks. These two networks were selected based on their superior performances demonstrated in previous studies [71][72][73][74][75][76]. In a recent study, Khan et al examined several semantic segmentation models including SegNet and DeepLabv3+ for smoke segmentation.…”