Perception Reinforcement Using Auxiliary Learning Feature Fusion: A
  Modified Yolov8 for Head Detection
Jiezhou Chen Guankun Wang Weixiang Liu Xiaopin Zhong Yibin Tian ZongZe Wu
Head detection provides distribution information of pedestrian, which is
crucial for scene statistical analysis, traffic management, and risk assessment
and early warning. However, scene complexity and large-scale variation in the
real world make accurate detection more difficult. Therefore, we present a
modified Yolov8 which improves head detection performance through reinforcing
target perception. An Auxiliary Learning Feature Fusion (ALFF) module comprised
of LSTM and convolutional blocks is used as the auxiliary task to help the
model perceive targets. In addition, we introduce Noise Calibration into
Distribution Focal Loss to facilitate model fitting and improve the accuracy of
detection. Considering the requirements of high accuracy and speed for the head
detection task, our method is adapted with two kinds of backbone, namely
Yolov8n and Yolov8m. The results demonstrate the superior performance of our
approach in improving detection accuracy and robustness.