Real-time pedestrian detection for automotive systems using an optimized YOLOv9 framework

FARP-net: a feature attention and residual pyramid network for pedestrian detection

Development of highway vehicle detection using background subtraction and Haar cascade methods

A streamlined vehicle detection framework that combines background subtraction for motion-oriented foreground extraction with a Haar cascade classifier for object identification in traffic video sequences is introduced, suggesting that classical computer vision techniques remain viable alternatives for real-time traffi...

Accuracy and Real-Time Performance Evaluation of Pedestrian Detection Models Based on the COCO Dataset

. The detection of pedestrians holds a very important position in application fields such as self-driving cars and intelligent monitoring systems. This research carries out a comparison between a traditional method of HOG+SVM and current deep learning models, including Faster R-CNN and YOLOv8, for the purpose of assess...

DECA-YOLO: Dual-pooling efficient channel attention for real-time pedestrian detection

Context-aware Bayesian Fusion for Multimodal Object Detection

This work proposes a novel context-aware multimodal late fusion approach that integrates a lightweight gating network within the probabilistic calibration framework and dynamically modulates the detection outputs of pre-trained object detectors based on the inferred context, without retraining of the detectors.

An Improved YoLov3 Framework For Real-Time Object Detection In Autonomous Vehicles

Optical vision is a crucial part of self-driving cars. Vehicle, street, building, pedestrian, and road sign detection that is accurate could help self-driving cars drive as safely as human beings. Object detection, on the other hand, has been a tough task for years due to the effects of lighting, topology, and occlusio...