RGB-based methods have certain disadvantages because they lack 3D information and cannot cope with the pose estimation problem in cluttered scenes. Therefore, we propose a region-level pose estimation network that uses RGB-D images. We first extracted the objects’ color features and geometric features by convolutional neural networks (CNN) and PointNet, respectively, and then performed feature fusion. The fused features were fed into a region-level feature extraction network to obtain the region-level features, which extracted the local geometry features from the point cloud and learned the point set’s semantic information. We used the output of the region-level feature extraction network to perform region-level pose estimation, then selected the pose with the highest confidence level as the output and iteratively optimized the pose to obtain the final results. The experimental results showed that the proposed solution performed well on the LINEMOD data set, which verified the effectiveness of the proposed method in the pose estimation problem and the algorithm’s robustness in severely cluttered scenes.

Generalized target detection algorithms perform well for large- and medium-sized targets but struggle with small ones. However, with the growing importance of aerial images in urban transportation and environmental monitoring, detecting small targets in such imagery has been a promising research hotspot. The challenge in small object detection lies in the limited pixel proportion and the complexity of feature extraction. Moreover, current mainstream detection algorithms tend to be overly complex, leading to structural redundancy for small objects. To cope with these challenges, this paper recommends the PCSG model based on yolov5, which optimizes both the detection head and backbone networks. (1) An enhanced detection header is introduced, featuring a new structure that enhances the feature pyramid network and the path aggregation network. This enhancement bolsters the model’s shallow feature reuse capability and introduces a dedicated detection layer for smaller objects. Additionally, redundant structures in the network are pruned, and the lightweight and versatile upsampling operator CARAFE is used to optimize the upsampling algorithm. (2) The paper proposes the module named SPD-Conv to replace the strided convolution operation and pooling structures in yolov5, thereby enhancing the backbone’s feature extraction capability. Furthermore, Ghost convolution is utilized to optimize the parameter count, ensuring that the backbone meets the real-time needs of aerial image detection. The experimental results from the RSOD dataset show that the PCSG model exhibits superior detection performance. The value of mAP increases from 97.1% to 97.8%, while the number of model parameters decreases by 22.3%, from 1,761,871 to 1,368,823. These findings unequivocally highlight the effectiveness of this approach.