Aiming at the problems of slow detection speed， low precision， missed detection and false detection of current forest pest detection methods， a forest pest detection method based on attention model and lightweight YOLOv4 was proposed. Firstly， a dataset was constructed and preprocessed by using geometric transformation， random color dithering and mosaic data augmentation techniques. Secondly， the backbone network of YOLOv4 was replaced with a lightweight network MobileNetV3， and the Convolutional Block Attention Module （CBAM） was added to the improved Path Aggregation Network （PANet） to build the improved lightweight YOLOv4 network. Thirdly， Focal Loss was introduced to optimize the loss function of the YOLOv4 network model. Finally， the preprocessed dataset was input into the improved network model， and the detection results containing pest species and location information were output. Experimental results show that all the improvements of the network contribute to the performance improvement of the model； compared with the original YOLOv4 model， the proposed model has faster detection speed and higher detection mean Average Precision （mAP）， and effectively solves the problem of missed detection and false detection. The proposed new model is superior to the existing mainstream network models and can meet the precision and speed requirements of real?time detection of forest pests.

Aiming at the problems of large model parameters， high computational complexity and low accuracy of traditional violence detection methods， a method of violence detection in video based on temporal attention mechanism and EfficientNet was proposed. Firstly， the foreground image obtained by preprocessing the dataset was input to the network model to extract the video features， meanwhile， the frame-level spatial features of violence were extracted by using the lightweight EfficientNet， and the global spatial-temporal features of the video sequence were further extracted by using the Convolutional Long Short-Term Memory （ConvLSTM） network. Then， combined with temporal attention mechanism， the video-level feature representations were obtained. Finally， the video-level feature representations were mapped to the classification space， and the Softmax classifier was used to classify the video violence and output the detection results， realizing the violence detection of video. Experimental results show that the proposed method can decrease the number of model parameters， reduce the computational complexity， increase the accuracy of violence detection and improve the comprehensive performance of the model with limited resources.