Browsing by Autor "Edwin Salcedo"

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A Novel Road Maintenance Prioritisation System Based on Computer Vision and Crowdsourced Reporting
(Multidisciplinary Digital Publishing Institute, 2022) Edwin Salcedo; Mona Jaber; Jesús Requena Carrión
The maintenance of critical infrastructure is a costly necessity that developing countries often struggle to deliver timely repairs. The transport system acts as the arteries of any economy in development, and the formation of potholes on roads can lead to injuries and the loss of lives. Recently, several countries have enabled pothole reporting platforms for their citizens, so that repair work data can be centralised and visible for everyone. Nevertheless, many of these platforms have been interrupted because of the rapid growth of requests made by users. Not only have these platforms failed to filter duplicate or fake reports, but they have also failed to classify their severity, albeit that this information would be key in prioritising repair work and improving the safety of roads. In this work, we aimed to develop a prioritisation system that combines deep learning models and traditional computer vision techniques to automate the analysis of road irregularities reported by citizens. The system consists of three main components. First, we propose a processing pipeline that segments road sections of repair requests with a UNet-based model that integrates a pretrained Resnet34 as the encoder. Second, we assessed the performance of two object detection architectures—EfficientDet and YOLOv5—in the task of road damage localisation and classification. Two public datasets, the Indian Driving Dataset (IDD) and the Road Damage Detection Dataset (RDD2020), were preprocessed and augmented to train and evaluate our segmentation and damage detection models. Third, we applied feature extraction and feature matching to find possible duplicated reports. The combination of these three approaches allowed us to cluster reports according to their location and severity using clustering techniques. The results showed that this approach is a promising direction for authorities to leverage limited road maintenance resources in an impactful and effective way.
ADRAS: Airborne Disease Risk Assessment System for Closed Environments
(Springer Science+Business Media, 2023) Wilber Rojas; Edwin Salcedo; Guillermo Sahonero-Alvarez
Computer Vision-Based Gait Recognition on the Edge: A Survey on Feature Representations, Models, and Architectures
(Multidisciplinary Digital Publishing Institute, 2024) Edwin Salcedo
Computer vision-based gait recognition (CVGR) is a technology that has gained considerable attention in recent years due to its non-invasive, unobtrusive, and difficult-to-conceal nature. Beyond its applications in biometrics, CVGR holds significant potential for healthcare and human-computer interaction. Current CVGR systems often transmit collected data to a cloud server for machine learning-based gait pattern recognition. While effective, this cloud-centric approach can result in increased system response times. Alternatively, the emerging paradigm of edge computing, which involves moving computational processes to local devices, offers the potential to reduce latency, enable real-time surveillance, and eliminate reliance on internet connectivity. Furthermore, recent advancements in low-cost, compact microcomputers capable of handling complex inference tasks (e.g., Jetson Nano Orin, Jetson Xavier NX, and Khadas VIM4) have created exciting opportunities for deploying CVGR systems at the edge. This paper reports the state of the art in gait data acquisition modalities, feature representations, models, and architectures for CVGR systems suitable for edge computing. Additionally, this paper addresses the general limitations and highlights new avenues for future research in the promising intersection of CVGR and edge computing.
Edge AI-Based Vein Detector for Efficient Venipuncture in the Antecubital Fossa
(Springer Science+Business Media, 2023) Edwin Salcedo; Patricia Peñaloza
Low-Cost Machine Vision System for Sorting Green Lentils (Lens Culinaris) Based on Pneumatic Ejection and Deep Learning
(2025) Davy Rojas Yana; Edwin Salcedo
This paper presents the design, development, and evaluation of a dynamic grain classification system for green lentils (Lens Culinaris), which leverages computer vision and pneumatic ejection. The system integrates a YOLOv8-based detection model that identifies and locates grains on a conveyor belt, together with a second YOLOv8-based classification model that categorises grains into six classes: Good, Yellow, Broken, Peeled, Dotted, and Reject. This two-stage YOLOv8 pipeline enables accurate, real-time, multi-class categorisation of lentils, implemented on a low-cost, modular hardware platform. The pneumatic ejection mechanism separates defective grains, while an Arduino-based control system coordinates real-time interaction between the vision system and mechanical components. The system operates effectively at a conveyor speed of 59 mm/s, achieving a grain separation accuracy of 87.2%. Despite a limited processing rate of 8 grams per minute, the prototype demonstrates the potential of machine vision for grain sorting and provides a modular foundation for future enhancements.
Smart Aquifer Monitoring in Fire-Prone Regions Using Ground-Level Wireless Sensor Networks: A Bolivian Case Study
(2025) Lucia Martinez-Zuzunaga; Edwin Salcedo; Fabio Díaz-Palacios; Mónica Guzmán-Rojo
Water management is becoming increasingly critical as the availability of this vital resource declines—a trend driven by human activity and forest fires. In the face of growing water scarcity, understanding aquifer dynamics is essential. Aquifer recharge is typically estimated using soil water balance models; however, these rely on detailed analyses of water distribution processes and are often constrained by uncertainties related to land surface changes and limited data availability. To address these challenges, this study presents the design, development, and preliminary results of a low-cost Internet of Things (IoT) system aimed at supporting aquifer recharge monitoring. The system comprises four end devices that continuously collect hydrological data, including albedo, soil moisture, temperature, and sap flow. Additionally, we implemented anomaly detection models to analyse the collected data in near real time for irregularities and sensor bursts. Initial findings from the system’s in situ deployment in the Chiquitania region of Santa Cruz, Bolivia, demonstrate its potential for continuous monitoring and prompt anomaly detection. This work represents a Phase I prototype with preliminary validation; absolute flux accuracy and species-specific calibration will be addressed in future campaigns.