The object detection task is to locate and classify objects in an image. The current state-of-the-art high-accuracy object detection algorithms rely on complex networks and high computational cost. These algorithms have high requirements on the memory resource and computing capability of the deployed device, and are difficult to apply to mobile and embedded devices. Through the depthwise separable convolution and multiple efficient network structures, this paper designs a lightweight backbone network and two different multiscale feature fusion structures, and proposes a lightweight one-stage object detection algorithm—MiniYOLO. With the model size of only 4.2 MB, MiniYOLO still maintains a high detection accuracy, realizing the trade-off between the model size and detection accuracy. Experimental results on MS COCO 2017 data set show that compared to the state-of-the-art PP-YOLO-tiny, MiniYOLO achieves higher mAP with the same model size. Compared with other lightweight object detection algorithms, MiniYOLO has certain advantages in detection accuracy or model size. The code associated with this paper can be downloaded from https://github.com/CaedmonLY/MiniYOLO/.

Nowadays, several image-based smart services have been widely used in our daily lives, generating many digital images. Since smart devices outsource digital images to the cloud, researchers prefer to select some desired targets from the massive images within the cloud for analysis and improve smart services. Therefore, protective image retrieval on the cloud has attained maximum concentration for privacy-preserving purposes, and the availability assurance of images on the cloud is also a crucial link. Ensuring image security and availability in the cloud environment and precisely preserving retrieval accuracy is comes as a utility-security dilemma while few existing works have explicitly addressed it. Therefore, this paper proposes privacy-preserving image retrieval in the distributed environment based on the combination of image encryption for similarity search and secret image sharing. On the basis of them, we define two-stage encryption. The first-stage encryption algorithm is introduced by modifying Wolfram's reversible cellular automata-based image encryption, which can create a set of processing images to ensure image security and retrieval accuracy. Then, the second-stage encryption algorithm is put forward based on secret image sharing to improve image security and availability. The color histogram could be extracted from the encrypted images for similarity retrieval, and the shadows could be extracted for similar image recovery. Security analysis demonstrates that image privacy and query privacy could be well protected. Moreover, the proposed work achieves more efficient performance for similarity search and similar image recovery compared with some recent works and realizes a reasonable retrieval accuracy on encrypted images for similarity search.