Long-distance tiny face detection based on enhanced YOLOv3 for unmanned system
This addresses the challenge of accurate and robust face detection in remote, high-density scenarios for unmanned systems, representing an incremental improvement.
The paper tackled the problem of long-distance tiny face detection for unmanned systems by proposing an enhanced YOLOv3 model (YOLOv3-C) that incorporates multi-scale features and feature fusion, achieving comparable performance to state-of-the-art YOLOv4 in tiny face detection tasks.
Remote tiny face detection applied in unmanned system is a challeng-ing work. The detector cannot obtain sufficient context semantic information due to the relatively long distance. The received poor fine-grained features make the face detection less accurate and robust. To solve the problem of long-distance detection of tiny faces, we propose an enhanced network model (YOLOv3-C) based on the YOLOv3 algorithm for unmanned platform. In this model, we bring in multi-scale features from feature pyramid networks and make the features fu-sion to adjust prediction feature map of the output, which improves the sensitivity of the entire algorithm for tiny target faces. The enhanced model improves the accuracy of tiny face detection in the cases of long-distance and high-density crowds. The experimental evaluation results demonstrated the superior perfor-mance of the proposed YOLOv3-C in comparison with other relevant detectors in remote tiny face detection. It is worth mentioning that our proposed method achieves comparable performance with the state of the art YOLOv4[1] in the tiny face detection tasks.