## 3 AI, DL and Computer Vision in UAV Sensing
### Deep Learning and Computer Vision
Generally, if we have the following dimensions:  
\(W_{1}\times H_{1}\times D_{1}\), then  
\(W_{2}=\frac{(W_{1}-F)}{S+1}\)  
\(H_{2}=\frac{(H_{1}-F)}{S+1}\)  
\(D_{2}=D_{1}\)  
Where \(W_{2}\), \(H_{2}\) and \(D_{2}\) are the width, height and depth of output. The final stages of a CNN structure for computer vision contain fully connected layers that respond to the input activation function [26]. These layers are made up of weights, biases, and neurons that are linked together and used to categorize images into numerous categories through training. The SoftMax activation function is often used for multi-class classification and is situated at the end of the fully connected layer in a CNN, whereas the Logistic activation function is used for binary classification. The final output layer displays labels that have been one-hot encoded. Two well-known types of CNNs commonly used on UAVs are VGG-16 and AlexNet:  
* VGG-16 is a highly accurate object identification and classification system, identifying 1000 photos from 1000 different categories with 92.7% accuracy. It is a popular picture classification technique that may be simply applied to transfer learning [27]. * In contrast, AlexNet is proficient in identifying objects that are not centered, as demonstrated by the fact that most of its top 5 classifications are appropriate for each frame. In 2012, AlexNet emerged as the winner of the ImageNet contest, boasting a top 5 error rate of only 15.33%, which was significantly lower than the 26.2% achieved by the runner-up. Additionally, AlexNet is acknowledged for pioneering deep learning in fields like medical image analysis and natural language processing [28]. Now, let us explore some examples and real-world applications of computer vision in tasks involving UAVs Various object detection benchmarks have been used to evaluate object detection algorithms. These benchmarks include datasets like the Daimler dataset [29], captured by a vehicle in an urban environment, with thousands of annotated pedestrians for training and testing. The Caltech dataset [30] consists of hours of videos recorded from a vehicle in regular urban traffic, with annotated bounding boxes representing pedestrians. The KITTI-D dataset focuses on evaluating detection algorithms for cars, pedestrians, and cyclists in autonomous driving scenarios. [PERSON] et al. [31] created a dataset with diverse car images from different locations, while the UA-DETRAC benchmark [32] provides a large dataset for vehicle detection. PASCAL VOC is a pioneering dataset [33] that standardized object detection, image classification, object segmentation, person layout, and action classification tasks. ImageNet [34] expanded on this work, significantly increasing the number of object classes and images available for training and evaluation. The MS COCO dataset, introduced by [PERSON] et al. [35], includes a vast number of images with manually segmented objects across various categories, offering more detailed annotations compared to ImageNet. Single-object tracking is a foundational challenge in computer vision, involving the estimation of a target's trajectory in a video sequence, given its initial state. Over the years, numerous datasets have been curated for evaluating single-object tracking algorithms. [PERSON] et al. [36] have developed a standardized platform to assess these algorithms, significantly increasing the dataset size from 50 to 100 sequences. Multi-object tracking, on the other hand, is a crucial research problem with diverse applications in surveillance, behavior analysis, and autonomous driving. Prominent multi-object tracking evaluation datasets include PETS09, PETS16, and KITTI-T [35]. Crop health monitoring is a crucial task performed by farmers daily to identify potential threats such as diseases, pests, and slow growth rates. Traditional methods involved visual inspection and manual collection of ground samples from random locations.