Application and Optimization of Lightweight Convolutional Neural Network in Target Detection

Due to its low computational complexity, minimal storage needs, and high real-time performance, lightweight convolutional neural networks (LCNN) have garnered significant attention amidst the swift advancement of deep learning technology. By automatically learning high-level feature representation, LCNN can capture key information in images more effectively. This paper aims to discuss the application and optimization of LCNN in target detection task. This paper discusses the key strategies such as network structure optimization, training method optimization and post-processing optimization to improve the performance of LCNN in target detection. The experimental results show that compared with the traditional SIFT-based target detection algorithm, LCNN has obvious advantages in detection success rate, time consumption and adaptability to different scenes. In addition, the lightweight design of LCNN makes it easier to deploy on equipment with limited resources. LCNN shows strong performance and wide application prospect in target detection, which is expected to provide new impetus for the innovation of target detection technology.