Over the passage of time Unmanned Autonomous Vehicles (UAVs), especially
 Autonomous flying drones grabbed a lot of attention in Artificial Intelligence.
 Since electronic technology is getting smaller, cheaper and more efficient, huge
 advancement in the study of UAVs has been observed recently. From monitoring
 floods, discerning the spread of algae in water bodies to detecting forest trail, their
 application is far and wide. Our work is mainly focused on autonomous flying
 drones where we establish a case study towards efficiency, robustness and accuracy
 of UAVs where we showed our results well supported through experiments.
 We provide details of the software and hardware architecture used in the study. We
 further discuss about our implementation algorithms and present experiments that
 provide a comparison between three different state-of-the-art algorithms namely
 TrailNet, InceptionResnet and MobileNet in terms of accuracy, robustness, power
 consumption and inference time. In our study, we have shown that MobileNet has
 produced better results with very less computational requirement and power consumption.
 We have also reported the challenges we have faced during our work
 as well as a brief discussion on our future work to improve safety features and
 performance. In modern era, UAVs have become very popular and have basic intelligence of being driven autonomously. Talking about ground traffic, these vehicles have limitations of physical paths and barriers. However, such is not the case with flying objects like drones as they do not suffer from such physical limitations. Autonomous Flying objects are in much of discussion these days and are striding all across the realm -traffic monitoring BID9 , agriculture BID11 , inventory management BID4 , surveillance BID15 , data mining, disaster response BID10 , etc. As their areas of application increase, it becomes more important to find algorithms well suited for these kind of vehicles. Some applications may not require the drone to be extremely accurate, but may require it to work for longer durations e.g. in surveillance applications while others may require it to be very precise but may not require it to work for long duration e.g. in delivery of items.In the last decade, significant changes have been observed in the field of autonomous motion planning of vehicles, UAVs in particular. The motion planning of UAVs are distinctly difficult because of several complexities that comes with aerial vehicles. The salience of differential constraints, uncertainty in the vehicle state and limited knowledge about the environment makes it impossible to have a precise pre-computed plan to follow through. These differences considerably gave rise to various approaches and techniques for planning the motion of these unmanned autonomous vehicles. The ambiguity of different algorithms and its inherent adversity pose an intriguing scope for a bench-marking study to enhance accuracy, robustness, power consumption, safety and inference time and fine tuning it further.Throughout this paper, we bear in mind some of the generic characteristics and prerequisites relating to UAVs. The basic design of a UAV is modelled to have acceleration and velocity constraints. Furthermore, the higher-order differential constraints also associate themselves with the equation of motion of a drone. However, the coherent objective involved in all UAVs is to guide the vehicle towards a goal. In this paper, we introduce to the best of our knowledge, a very first comparative study of three algorithms in order to find a better motion control of a drone for detecting a trail.In order to be able to compare a set of algorithms in a meticulous way, it is necessary to establish their precision and robustness and to evaluate its power consumption as well as inference time. Along with these metrics established for a particular algorithm, it is also necessary to consider the distinct areas of its application. Only then, based on the requirements called for by a particular application, a reasonable opinion about an algorithm is to be formed. Our study covers recent developments and algorithms used in the area of trail detection by UAVs and runs down as comprehensively as possible what has been already upheld regarding these algorithms. While producing this work, we have encountered several challenges. Few of these challenges are listed below:1. The major challenge encountered was to run our DNN models on the physical drone in real time due to a hardware bug we were facing with the FCU.2. We could not train our models from scratch due to lack of significant amount of dataset. Additionally, we handled a lot of issues to make our models more stable and robust. Since in each trial, the number of images in each class (left, straight and right) were different, there was a lot of data imbalance which we solved by upsampling and downsampling the dataset.3. Due to the large number of training parameters at the beginning, our models were overfitted. We eliminated over-fitting by introducing several data augmentation techniques (random flipping, random rotation, random contrast and transition etc. ). We further included regularization (especially dropout layers) in order to reduce network complexity.4. Power is one of the important factors especially in mobile embedded devices with small size and computational power. Typically, deep learning algorithms consume more power specifically for the real time inference. We have made an estimate of the power consumption of each of our model by calculating the GPU power drawn by them but we could not test how long our drone would run implementing each of these models due to the hardware bug mentioned before. In this paper, we have presented a comparison between 3 algorithms -TrailNet, InceptionResnet and MobileNet in terms of accuracy, computational cost, power consumption, inference time and robustness. The choice of algorithm for UAVs varies on the basis of several factors. In our work, we have worked with some of the factors which we thought would be pivotal in algorithm selection considering reasonable comparisons. We observed in our study that MobileNet outperformed others with very less computational requirement and power consumption. Hence in our opinion, MobileNet is more befitting for drones and other embedded devices compared to TrailNet and InceptionResnet.Safety is another major concern in terms of drones. There can be many situations such as collision with the objects, external disturbances like winds, chances of drone moving out of manual controller zone, battery issues, chances of getting stolen and other safety hazards. We will be implementing these drone related safety features in our future work.
case study on optimal deep learning model for UAVs
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