Application of Computer Vision to image review process in the software development

The main goal of reviews in software development is to grant the quality and correctness of the products. One of those tasks that we can highlight is image review. This process could require more time to process and tends to be less accurate due to human error. Considering that developers who perform this type of task also have other responsibilities, the longer the review takes, the more work time is required, and this can affect the time of deliverables in the industry. Therefore, the proposal of this research is to use Optical Character Recognition (OCR) and Computer Vision (CV), to automate the process of image review, for the interpretation and validation of the content, both textual and objects. To carry out this research a set of 100 images was used in manual and automated review process and was verified that the automated approach reduced the execution time by 96.45%, and reduced the number of errors by 4%, proving that it is feasible in this development context. The application of this tool helped to increase efficiency by reducing the time spent on manual reviews, increased the consistency of image analysis, ensuring better accuracy compared to manual reviews. It also promoted improvements in development and the team, through a more productive work environment.

Authors

Institutions

Publication Details

Published
2025-09-17
DOI
https://doi.org/10.5753/sast.2025.13969
Primary Topic
Image Retrieval and Classification Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Application of Computer Vision to image review process in the software development

Daniel Henrique de Souza, Vinícius Silva, E. Furtado De Simas Filho, Wiliam Correa Marques et al.
Image Retrieval and Classification Techniques
article

Application of Computer Vision to image review process in the software development

Daniel Henrique de Souza, Vinícius Silva, E. Furtado De Simas Filho, Wiliam Correa Marques, Edluce Leitão Veras, Andrezza de Melo Bonfim
article en

Abstract

The main goal of reviews in software development is to grant the quality and correctness of the products. One of those tasks that we can highlight is image review. This process could require more time to process and tends to be less accurate due to human error. Considering that developers who perform this type of task also have other responsibilities, the longer the review takes, the more work time is required, and this can affect the time of deliverables in the industry. Therefore, the proposal of this research is to use Optical Character Recognition (OCR) and Computer Vision (CV), to automate the process of image review, for the interpretation and validation of the content, both textual and objects. To carry out this research a set of 100 images was used in manual and automated review process and was verified that the automated approach reduced the execution time by 96.45%, and reduced the number of errors by 4%, proving that it is feasible in this development context. The application of this tool helped to increase efficiency by reducing the time spent on manual reviews, increased the consistency of image analysis, ensuring better accuracy compared to manual reviews. It also promoted improvements in development and the team, through a more productive work environment.

Institute of Technology of Cambodia (KH), Institute of Science and Technology
Samsung Eletrônica da Amazônia, Samsung
Openalex Percentile: Top 75%
Image Retrieval and Classification Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.