Abstract
Abstract: To cut down on occlusion-induced false detections of vehicle targets, an improved vehicle detection strategy based on a more advanced YOLO network is proposed. The proposed method makes use of the Flip-Mosaic algorithm to enhance the network's perception of small targets. A multi-type vehicle target dataset was developed using data from a variety of scenarios. The dataset served as the foundation for the detection model's training. Experiments showed that the Flip-Mosaic data enhancement algorithm reduced false detection rates and improved vehicle detection accuracy.
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More From: International Journal for Research in Applied Science and Engineering Technology
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