Real-time detection and identification of orchard pests is related to the economy of the orchard industry. Using lab picture collections and pictures from web crawling, a dataset of common pests in orchards has been created. It contains 24,748 color images and covers seven types of orchard pests. Based on this dataset, this paper combines YOLOv5 and GhostNet and explains the benefits of this method using feature maps, heatmaps and loss curve. The results show that the mAP of the proposed method increases by 1.5% compared to the original YOLOv5, with 2$\times$ or 3$\times$ fewer parameters, less GFLOPs and the same or less detection time. Considering the fewer parameters of the Ghost convolution, our new method can reach a higher mAP with the same epochs. Smaller neural networks are more feasible to deploy on FPGAs and other embedding devices which have limited memory. This research provides a method to deploy the algorithm on embedding devices.

Face recognition payment is a new type of payment method, with AI face recognition technology as the core, and its speed and convenience are more in line with the users’ payment habits. However, the face is a biological feature with weak privacy, and the protection of user information security is particularly important. At present, face payment technology still has security risks, and the data transmitted during the transaction process are vulnerable to attacks. Aiming at the security problems in the payment process, a payment system that is jointly encrypted by the SM4 algorithm and the face liveness detection algorithm was proposed in this paper, which supports a variety of communication methods. The hardware platform adopts an octa-core 64-bit ARM processor with a main frequency of 1.8 GHz, which has powerful computing and processing capabilities. Based on the Android intelligent operating system, the development environment is more secure and convenient. It is also equipped with a liveness detection 3D structured light camera, which dynamically collects face information and accurately analyzes the characteristics of living bodies. Through the data encryption and decryption test and face performance index detection, the expected effect of the system was achieved, which greatly improved the performance of the face payment system currently studied. The SM4 encryption algorithm improved the running rate of encrypted data and the security of face transaction data transmission, the face detection algorithm improved the accuracy of living body feature recognition, and the payment system effectively improved the accuracy and security of face payment.

The species and population size of marine fish are important for maintaining the ecological environment and reflecting climate change. Traditional fish detection methods mainly rely on manual or traditional computer vision, which has disadvantages such as complex design, low detection accuracy, and poor generalization. The widespread use of ocean observation systems has accumulated a large number of images and videos, which makes the application of deep learning on marine fish detection possible. In this paper, a real-time high-precision fish detection algorithm based on YOLOv5s is constructed. Considering the enhancement of the channel representation and spatial interaction ability of the model, the attention mechanism and gated convolution are introduced, respectively, and GhostNet is introduced to lighten the model. Through a series of model comparisons, two improved models, S-Head-Ghost-Fish9 and S-SE-HorBlock-Head-Ghost-Fish9, are finally obtained. Compared with the original model, in terms of model size, the former reduces by 19% and the latter increases by 9.5%; in terms of computation, the former reduces by 15.7% and the latter reduces by 3.1%; in terms of detection speed, both take about 17 ms to detect a single image, and both can meet the real-time detection requirements; in terms of detection accuracy, the former improves by 3% and the latter by 3.6%. Compared with the latest detection algorithms of YOLOv6 and YOLOv8, the detection accuracy is slightly lower than 1%, but the model size and computation amount are only 1/3 to 1/2 of them. The improved models can help assess the population size and growth of the fish, which is of great significance in maintaining the stability of the fish population.