Abstract
This article focuses on object detection in challenging environments, where objects of interest need to be detected in images captured under unconstrained conditions. These environments can include outdoor scenes with varying lighting, weather conditions, and background clutter. Object detection in such scenarios is crucial for applications like autonomous driving, surveillance, and industrial production inspection. The paper explores existing techniques for object detection in challenging environments and proposes novel solutions to enhance its performance. One key aspect emphasized in the paper is the need for relevant datasets to validate models under unconstrained scenarios. While several datasets already exist, the research identifies gaps in specific situations and addresses them by proposing new datasets, such as the Indian Lane Dataset for autonomous vehicles. The proposed object detection framework leverages spatial information to detect objects in challenging environments, and its performance is evaluated against benchmark datasets and the newly proposed datasets. A real-world application demonstrates significant improvements in object detection performance in natural environments.
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