To ensure the quality and reliability of polymer lithium-ion battery (PLB), automatic blister defect detection instead of manual detection is developed in the production of PLB cell sheets. A convolutional neural network (CNN) based detection method is proposed to detect blister in cell sheets employing cell sheet images. An improved architecture for dense block and a learning method based on optimization of learning rate are discussed. The proposed method was superior to other machine learning based methods when the classification performance and confusion matrix were compared in experiments. The proposed CNN method had the best defect detection performance and real-time performance for industry field application.

Unmanned surface vehicles (USV) is the development trend of future ships, and it will be widely used in various kinds of marine tasks. Obstacle avoidance is one key technology for autonomous navigation of USV. Convolutional neural network based obstacle classification and detection method is applied to USV visual images in environment sensing task. To solve the problem of low detection and classification accuracy of obstacles in the visual inspection of USV, a bidirectional feature pyramid networks is proposed combining hybrid network architecture of ResNet and improved DenseNet. The proposed method can further enhance the detection and classification some types of obstacles by using the underlying multi-layer detail features and high-level strong semantic features in the network architecture. The detection and classification performance of the proposed method is evaluated on a self built dataset. Ablation experiments and performance tests on open datasets are also employed. The experimental results show that the proposed algorithm has best performance for obstacles detection, and it is more suitable for autonomous navigation of USV.