Research on Algorithm Optimization of Multi-Object Recognition in Complex Scene

Target detection based on deep learning is aiming to automatically detect and locate target objects in images or videos using deep learning technology, and there are still some challenges to be addressed. Firstly, in complex scenes, target detection models often have difficulty fully extracting features from input images. Due to the fact that certain targets have ambiguous features and are easily affected by background interference, this makes the model's accuracy in identifying and locating targets compromised. Secondly, current semantic segmentation models may have difficulty distinguishing between objects of different categories in complex scenes, leading to confusion in segmentation results and reducing accuracy and robustness. In response to these issues, this paper will focus on researching and implementing a multi-target recognition system, and compare and improve on several typical deep learning target recognition algorithms. Firstly, the mainstream target recognition algorithms and network structures are analyzed, and the pros and cons of various algorithms are evaluated comprehensively. Then, the YOLO model is optimized end-to-end, and its network structure is improved to reduce the miss detection rate of multi-target recognition in complex scenes and improve the precision of recognizing small targets. Furthermore, TensorRT technology is studied, where the neural network is optimized and the trained multi-target recognition model is optimized for deployment based on target platform parameters, thus significantly improving inference speed and enhancing the system's recognition efficiency and real-time performance. Finally, through the analysis of experimental results, the optimized deep learning multi-target recognition model can effectively realize real-time multi-target recognition and localization functions, showing better recognition performance and a significant improvement in inference speed, meeting the real-time recognition requirements.