To improve the recognition accuracy of coal gangue images with the back propagation (BP) neural network, a coal gangue image recognition method based on BP neural network and ASGS-CWOA (ASGS-CWOA-BP) was proposed, which makes two key contributions. Firstly, a new feature extraction method for the unique features of coal and gangue images is proposed, known as “Encircle–City Feature”. Additionally, a method that applied ASGS-CWOA to optimize the parameters of the BP neural network was introduced to address to the issue of its low accuracy in coal gangue image recognition, and a BP neural network with a simple structure and reduced computational consumption was designed. The experimental results showed that the proposed method outperformed the other six comparison methods, with recognition of 95.47% and 94.37% in the training set and the test set, respectively, showing good symmetry.
LiDAR sensor is a valuable tool for environmental perception as it can generate 3D point cloud data with reflectivity and position information by reflecting laser beams. However, it cannot provide the meaning of each point cloud cluster, which has led to many studies focusing on identifying semantic information about point clouds. This paper explores point cloud segmentation and presents a network that encodes point cloud data at different levels to obtain semantic information about the point cloud cluster. The local context awareness network uses the points and their surrounding points to contribute local features, which are then combined with global features to obtain a better understanding of the position, density, and other information of the point cloud. The feature extraction network provides highly abstracted information, allowing for more accurate semantic segmentation of the discrete points in space. The proposed algorithm is compared and verified against other Semantic KITTI data algorithms, and has achieved state-of-the-art performance. Due to its ability to note fine-grained features on the z-axis in space, the algorithm shows higher prediction accuracy for certain types of objects. Moreover, the training and validation time is short, and the algorithm can meet high real-time requirements for 3D perception tasks.
A LiDAR sensor is a valuable tool for environmental perception as it can generate 3D point cloud data with reflectivity and position information by reflecting laser beams. However, it cannot provide the meaning of each point cloud cluster, so many studies focus on identifying semantic information about point clouds. This paper explores point cloud segmentation and presents a lightweight convolutional network called Fast Context-Awareness Encoder (FCAE), which can obtain semantic information about the point cloud cluster at different levels. The surrounding features of points are extracted as local features through the local context awareness network, then combined with global features, which are highly abstracted from the local features, to obtain more accurate semantic segmentation of the discrete points in space. The proposed algorithm has been compared and verified against other semantic KITTI data algorithms and has achieved state-of-the-art performance. Due to its ability to note fine-grained features on the z-axis in space, the algorithm shows higher prediction accuracy for certain types of objects. Moreover, the training and validation time is short, and the algorithm can meet high real-time requirements for 3D perception tasks.
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