Redefining Machine Learning Techniques and Object Detection to Increase Accuracy and Efficiency: Review

Abstract

Deep learning has significantly improved machine vision object detection, especially for applications like surveillance, environmental monitoring, and UAV data interpretation. This study examines machine learning methods for object identification, focusing on image, instance, object, and semantic segmentation. It emphasizes the importance of deep CNNs, which utilize convolutional layers, pooling, and non-linear activation to enhance accuracy and speed. Backbone networks face the challenge of balancing performance and efficiency. Techniques like handling uneven sampling, localization, optimization, and cascade learning play a crucial role in improving detection. Data analysis intensification further supports model refinement. Enhancements such as test-stage model acceleration and duplication elimination boost detection speed and accuracy. The use of pyramidal and mirror structures enhances object recognition, making these techniques essential for improving detection in machine vision systems. Overall, deep learning, especially deep CNNs and GPUs, continues to advance the field of object detection.