Anchor-Free Person Target Detection Algorithm Based on Heat Map Prediction
Traditional target detection algorithms usually preset a number of candidate target bounding boxes(referred to as anchor boxes)to exhaustively cover potential target locations.Afterward,they filter out redundant bounding boxes to determine the target's location by calculating the confidence scores of the remaining boxes.This detection method requires complex post-processing and has low detection efficiency.To address these issues,this study proposes an anchor-free target detection algorithm based on heat map prediction.The algorithm transforms the detection of the character target into the detection of the maximum value on a character heat map,identifying the target's center point.By regressing the target size from the center point,the final target position can be determined,eliminating the need for anchor boxes and their associated calculations.This approach effectively reduces computational costs and significantly improves detection speed.Experimental results show that,compared to traditional anchor-based detection algorithms like Faster R-CNN and SSD,the proposed algorithm increases target detection speed to 45 frame/s.Additionally,detection accuracy on various datasets is significantly improved over the previous methods.In realistic scenes tests,the algorithm can track and accurately detect character positions in real-time,even in scenarios with character occlusions,achieving the goal of real-time detection.
target detectionanchor-freeheat mapcenter point detectionhourglass network
国家科技期刊平台
NSTL
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