A CASE STUDY ON OPTIMAL DEEP LEARNING MODEL FOR UAVS
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.
 The paper initiates a comparison between different SOTA convolutional neural networks for UAV trail guidance with the goal of finding a better motion control for drones. They use a simulator (but not a physical UAV) to perform their experiments, which consisted on evaluating tuned versions of Inception-Resnet and MobileNet models using the IDSIA dataset, achieving good results in the path generated. I think that the authors have perform an interesting evaluation framework, although not novel enough according to the literature. It is also great that the authors have included an explicit enumeration of all the dimensions relevant for their analysis (which are sometimes neglected), namely, computational cost, power consumption, inference time and robustness, apart from accuracy. However, I think the paper is not very well polished: there are quite a lot of grammatical, typing and aesthetic errors. Furthermore, the analysis performed is an A+B approach from previous works (Giusti et al.2016, and Smolyanskiy et al, 2017) and, thus, it is hard to find the novelty here, since similar comparisons have been already performed. Therefore, the paper needs major improvements in terms of clarity regarding the motivations in the introduction. Also, one third of the paper is devoted to the software and hardware architecture used in the study, which I think it would be better fitted in an appendix section as it is of no added scientific value. Another weakpoint is that the authors were unable to run their DNN models on a physical drone in real time due to a hardware bug... I think the paper would benefit from a more robust (real) experimentation since, as they are, the presented results and experiments are far from conclusive.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A CASE STUDY ON OPTIMAL DEEP LEARNING MODEL FOR UAVS
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The paper initiates a comparison between different SOTA convolutional neural networks for UAV trail guidance with the goal of finding a better motion control for drones. They use a simulator (but not a physical UAV) to perform their experiments, which consisted on evaluating tuned versions of Inception-Resnet and MobileNet models using the IDSIA dataset, achieving good results in the path generated. I think that the authors have perform an interesting evaluation framework, although not novel enough according to the literature. It is also great that the authors have included an explicit enumeration of all the dimensions relevant for their analysis (which are sometimes neglected), namely, computational cost, power consumption, inference time and robustness, apart from accuracy. However, I think the paper is not very well polished: there are quite a lot of grammatical, typing and aesthetic errors. Furthermore, the analysis performed is an A+B approach from previous works (Giusti et al.2016, and Smolyanskiy et al, 2017) and, thus, it is hard to find the novelty here, since similar comparisons have been already performed. Therefore, the paper needs major improvements in terms of clarity regarding the motivations in the introduction. Also, one third of the paper is devoted to the software and hardware architecture used in the study, which I think it would be better fitted in an appendix section as it is of no added scientific value. Another weakpoint is that the authors were unable to run their DNN models on a physical drone in real time due to a hardware bug... I think the paper would benefit from a more robust (real) experimentation since, as they are, the presented results and experiments are far from conclusive.
assistant
 The paper initiates a comparison between different SOTA convolutional neural networks for UAV trail guidance with the goal of finding a better motion control for drones. They use a simulator (but not a physical UAV) to perform their experiments, which consisted on evaluating tuned versions of Inception-Resnet and MobileNet models using the IDSIA dataset, achieving good results in the path generated. I think that the authors have perform an interesting evaluation framework, although not novel enough according to the literature. It is also great that the authors have included an explicit enumeration of all the dimensions relevant for their analysis (which are sometimes neglected), namely, computational cost, power consumption, inference time and robustness, apart from accuracy. However, I think the paper is not very well polished: there are quite a lot of grammatical, typing and aesthetic errors. Furthermore, the analysis performed is an A+B approach from previous works (Giusti et al.2016, and Smolyanskiy et al, 2017) and, thus, it is hard to find the novelty here, since similar comparisons have been already performed. Therefore, the paper needs major improvements in terms of clarity regarding the motivations in the introduction. Also, one third of the paper is devoted to the software and hardware architecture used in the study, which I think it would be better fitted in an appendix section as it is of no added scientific value. Another weakpoint is that the authors were unable to run their DNN models on a physical drone in real time due to a hardware bug... I think the paper would benefit from a more robust (real) experimentation since, as they are, the presented results and experiments are far from conclusive.