Multi-scale and partial occlusions in infrared pedestrian images for target detection make it difficult for traditional algorithms to achieve accurate detection. This study developed a cross-scale infrared pedestrian detection algorithm based on a dynamic feature optimization mechanism. First， to alleviate the limitation that pedestrian target features are difficult to express effectively in complex environments， which results in low target detection accuracy， a dynamic feature optimization mechanism is presented. The luminance perception module and EG-Chimp optimization model are designed to enhance the local contrast of the input image and suppress background information. Second， the CSPdarknet53 structure is utilized as the backbone feature extraction network. Accordingly， a CSFF-BiFPN feature pyramid structure and cross-scale feature fusion module are constructed to improve the detection accuracy of multi-scale and partially occluded pedestrian targets. Finally， the CIOU loss function is introduced to accelerate network convergence rate and improve detection performance to locate pedestrian targets more accurately. To verify the advantages of the proposed detection network， nine classical detection algorithms are selected as baseline methods and tested on KAIST datasets. Experimental results demonstrate that the proposed algorithm can accurately detect multi-scale and partially occluded infrared pedestrian targets in complex environments， with detection accuracies of up to 90.7 %.