Technical Reports

Tue Mar 31 2026

Evolution of Segmentation Models in Our Computer Vision Pipeline

This report outlines the progression of segmentation models used in a computer vision pipeline, moving from SegDecNet to RTMDet and eventually SegFormer. Each transition reflects growing project demands for better accuracy, faster inference, and improved handling of complex visual scenes, highlighting how modern architectures combine efficiency, scalability, and contextual understanding.

Sat Mar 28 2026

Evolution of the Object Detection Pipeline

Migration from Detectron2/YOLO to RFDetR for Enhancing Precision, Latency and Architectural Efficiency

Wed Dec 31 2025

Waterhemp Detection in Corn Field by using a Semantic Segmentation model

This article explains how a semantic segmentation model was used to detect waterhemp in corn fields using drone imagery. The approach identifies three categories—waterhemp, healthy corn, and stressed corn—providing farmers with detailed insights for better crop management. It covers data collection, image analysis, annotation, model results, and future improvements such as using multispectral imagery.

Thu Jan 01 2026

14 Free Datasets for Agricultural Crop Detection

This article presents a curated collection of 14 free, drone-based agricultural datasets useful for training machine learning models in crop detection and segmentation. It highlights dataset sizes, classes, licenses, preprocessing steps, and augmentation methods for crops such as corn, potato, soybean, tobacco, cotton, rice, cabbage, radish, and sugar beet. The list is designed to help researchers and developers working on agricultural AI applications find suitable training data.