Aiming at the problem of complex traffic intersection images， the difficulty in detecting small targets， and the tendency for occlusion between targets， as well as the color distortion， noise， and blurring caused by changes in weather and lighting， a multi-target detection algorithm ITD-YOLOv9（Intersection Target Detection-YOLOv9） for traffic intersection images based on YOLOv9 （You Only Look Once version 9） was proposed. Firstly， the CoT-CAFRNet （Chain-of-Thought prompted Content-Aware Feature Reassembly Network） image enhancement network was designed to improve image quality and optimize input features. Secondly， the iterative Channel Adaptive Feature Fusion （iCAFF） module was added to enhance feature extraction for small targets as well as overlapped and occluded targets. Thirdly， the feature fusion pyramid structure BiHS-FPN （Bi-directional High-level Screening Feature Pyramid Network） was proposed to enhance multi-scale feature fusion capability. Finally， the IF-MPDIoU （Inner-Focaler-Minimum Point Distance based Intersection over Union） loss function was designed to focus on key samples and enhance generalization ability by adjusting variable factors. Experimental results show that on the self-made dataset and SODA10M dataset， ITD-YOLOv9 algorithm achieves 83.8% and 56.3% detection accuracies and 64.8 frame/s and 57.4 frame/s detection speeds， respectively； compared with YOLOv9 algorithm， the detection accuracies are improved by 3.9 and 2.7 percentage points respectively. It can be seen that the proposed algorithm realizes multi-target detection at traffic intersections effectively.