Research on Small Target Detection Algorithm of Convolutional Neural Networks Based on Receptive Field

With the continuous renovation of computer vision and deep learning technologies, object detection has emerged as a crucial research orientation in the contemporary field of artificial intelligence. Among numerous detection targets, small objects pose a technical challenge for detection due to their small pixel proportion, scarce information content, and vulnerability to external interferences and occlusions. This paper conducts an in-depth exploration of the key technologies for small object detection and proposes a small object detection algorithm model of convolutional neural network based on receptive fields. Through the improvement of the Inception module and the design of a novel receptive field enhancement structure, the convolutional neural network can cover the small object areas in the image more effectively during the feature extraction process, thereby enhancing the recognition ability for small objects. Simultaneously, a feature fusion module is established. The core mechanism of this module lies in adopting a multi-scale approach to transform and integrate the feature points extracted from each convolutional layer. By fusing the feature data of various network layers, this model can extract the details of the image more profoundly, thereby enhancing the detection accuracy of small objects. Compared with other similar methods in VOC 2007 test set, the proposed model has increased the mAP value by 0.5% to 18.4%, while maintaining a faster detection rate. On the MS COCO dataset, compared with other algorithms, the proposed algorithm has achieved an improvement of 1.6%~13.2% in mAP value, which fully verifies the effectiveness and practical value of the algorithm.