Enhancing Object Detection in Complex Environments Using Deep Convolutional Neural Networks

Abstract

Object detection in complex environments, such as urban settings or natural landscapes, presents significant challenges due to occlusions, varying illumination, and diverse object scales. Traditional object detection methods often struggle with these complexities, resulting in suboptimal performance. Deep Convolutional Neural Networks (CNNs) have demonstrated remarkable potential in overcoming these limitations by learning intricate feature hierarchies directly from data. This paper explores the advancements in object detection using CNNs, focusing on techniques that enhance detection accuracy in complex environments. We propose a novel CNN-based architecture that integrates multi-scale feature extraction and attention mechanisms to improve the robustness and precision of object detection. The proposed model is evaluated on standard benchmarks, showing significant improvements over existing state-of-the-art methods in terms of accuracy and computational efficiency.