With the development of self-media, the burden of client-side computation and storage of massive data has become increasingly heavy. Additionally, considering the presence of sensitive information in images, image owners commonly adopt the practice of encrypting images before storing them in the cloud. However, encrypted image retrieval faces a challenge of striking a balance between security and efficiency. To address this issue, a Multi-label Privacy-preserving Image Retrieval scheme based on Object Detection (MPIR-OD) is proposed. Firstly, image labels are extracted using object detection techniques. Then, frequent itemsets of labels are discovered through mining label association rules, and they are matched and classified with the previously extracted image labels to construct an index. Lastly, the Asymmetric Scalar-product Preserving Encryption (ASPE) is employed to encrypt image feature vectors, ensuring the privacy of the images, and enabling secure K-Nearest Neighbor (KNN) operations using the ASPE algorithm. Compared to existing schemes, the MPIR-OD scheme achieves a reduction in retrieval time of approximately 6 times and an improvement in retrieval accuracy of around 15.