In recent years, increasing image data comes from various sensors, and object detection plays a vital role in image understanding. For object detection in complex scenes, more detailed information in the image should be obtained to improve the accuracy of detection task. In this paper, we propose an object detection algorithm by jointing semantic segmentation (SSOD) for images. First, we construct a feature extraction network that integrates the hourglass structure network with the attention mechanism layer to extract and fuse multi-scale features to generate high-level features with rich semantic information. Second, the semantic segmentation task is used as an auxiliary task to allow the algorithm to perform multi-task learning. Finally, multi-scale features are used to predict the location and category of the object. The experimental results show that our algorithm substantially enhances object detection performance and consistently outperforms other three comparison algorithms, and the detection speed can reach real-time, which can be used for real-time detection.

Most current pose estimation methods have a high resource cost that makes them unusable in some resource-limited devices. To address this problem, we propose an ultra-lightweight end-to-end pose distillation network, which applies some helpful techniques to suitably balance the number of parameters and predictive accuracy. First, we designed a lightweight one-stage pose estimation network, which learns from an increasingly refined sequential expert network in an online knowledge distillation manner. Then, we constructed an ultra-lightweight re-parameterized pose estimation subnetwork that uses a multi-module design with weight sharing to improve the multi-scale image feature acquisition capability of the single-module design. When training was complete, we used the first re-parameterized module as the deployment network to retain the simple architecture. Finally, extensive experimental results demonstrated the detection precision and low parameters of our method.

Accurate lightweight pose estimation is still a difficult challenging task influenced by different human poses and various complex backgrounds in 2D human images. To address the above problems, we propose a Lightweight Single-branch Pose Distillation network, termed LSPD, which is a lightweight powerful fully convolutional pose network that can be executed quickly with a low computational cost for accurate pose estimation. First, we introduced an efficient end-to-end pose distillation sequence framework, which utilizes a small number of lightweight and strong pose estimation stages to effectively transfer the pose knowledge of our teacher model. Second, we constructed a compact and strong pose estimation stage that uses a type of lightweight multi-scale residual block to enhance the image features and the image-dependent spatial features representation ability of the model. At the same time, it reduces the computational cost. Finally, when training is complete, we used the backbone network and the first student stage as the simple architecture to deploy. Extensive experiments demonstrated that the proposed method obtains excellent performance with high accuracy and low model parameters.