Object detection technology is a popular research direction which is widely used in areas such as autonomous driving and medical diagnosis. At this stage mobile devices often have limited storage resources to deploy large object detection networks and need to meet real-time requirements. This paper proposes a lightweight and efficient object detection model based on YOLOv4, first using the lightweight network GhostNet to extract image features and reduce the number of parameters and computation of the backbone structure; then combining AFmodule and Meta-ACON activation function to enhance the feature extraction capability of the backbone network, which strengthen the mode’s ability to capture image spatial feature information; this paper also designs the RL-PAFPN feature fusion structure is with the Reslayer module to further improve the model’s ability to extract and fuse image features. By comparing other mainstream object detection models, the YOLOv4-Ghost-AMR network in this paper has less computation and fewer parameters, and the accuracy of the model reaches 86.83%, which is suitable for deployment in mobile devices with limited storage. The model proposed in this paper can be applied to medical, traffic and fault detection fields, which changes the traditional manual detection method and saves manpower and time costs, achieving high precision real-time object detection.

Sentiment analysis of one modality (e.g., text or image) has been broadly studied. However, not much attention has been paid to the sentiment analysis of multi-modal data. As the research on and applications of multi-modal data analysis are becoming more and more broad, it is necessary to optimize BERT internal structure. This article proposes a hierarchical multi-head self-attention and gate channel BERT, which is an optimized BERT model. The model is composed of three modules: the hierarchical multi-head self-attention module realizes the hierarchical extraction process of features; the gate channel module replaces BERT’s original Feed Forward layer to realize information filtering; and the tensor fusion model based on a self-attention mechanism is utilized to implement the fusion process of different modal features. Experiments show that our method achieves promising results and improves accuracy by 5–6% when compared with traditional models on the CMU-MOSI dataset.