Lessons Learned Report: Super-Resolution for Detection Tasks in Engineering Problem-Solving
We describe the lessons learned from targeting agricultural detection problem-solving, when subject to low resolution input maps, by means of Machine Learning-based super-resolution approaches. The underlying domain is the so-called agro-detection class of problems, and the specific objective is to learn a complementary ensemble of sporadic input maps. While super-resolution algorithms are branded with the capacity to enhance various attractive features in generic photography, we argue that they must meet certain requirements, and more importantly, that their outcome does not necessarily guarantee an improvement in engineering detection problem-solving (unlike so-called aesthetics/artistic super-resolution in ImageNet-like datasets). By presenting specific data-driven case studies, we outline a set of limitations and recommendations for deploying super-resolution algorithms for agro-detection problems. Another conclusion states that super-resolution algorithms can be used for learning missing spectral channels, and that their usage may result in some desired side-effects such as channels' synchronization.
Code (0)
등록된 구현이 없습니다.
Tasks
Super-ResolutionSimilar Papers 제목 키워드 기반
The Singularity Controversy, Part I: Lessons Learned and Open Questions: Conclusions from the Battle on the Legitimacy of the Debate
This report seeks to inform policy makers on the nature and the merit of the arguments for and against the concerns associated with a potential technological singularity. Part I describes the lessons learned from our i…
MisconceptionsSPIRA: Building an Intelligent System for Respiratory Insufficiency Detection
Respiratory insufficiency is a medic symptom in which a person gets a reduced amount of oxygen in the blood. This paper reports the experience of building SPIRA: an intelligent system for detecting respiratory insufficie…
A Comprehensive Survey of Transformers for Computer Vision
As a special type of transformer, Vision Transformers (ViTs) are used to various computer vision applications (CV), such as image recognition. There are several potential problems with convolutional neural networks (CNNs…
Anomaly DetectionDenoisingimage-classificationImage Classification+9Digitization of Pathology Labs: A Review of Lessons Learned
Pathology laboratories are increasingly using digital workflows. This has the potential of increasing lab efficiency, but the digitization process also involves major challenges. Several reports have been published descr…
ManagementReusability in MLOps: Leveraging Ports and Adapters to Build a Microservices Architecture for the Maritime Domain
ML-Enabled Systems (MLES) are inherently complex since they require multiple components to achieve their business goal. This experience report showcases the software architecture reusability techniques applied while buil…
Anomaly Detection