Research on Online Detection of Small Foreign Objects Using Few-Shot Learning and Attention-Based End-to-End Networks
Small object detection is one of the research directions in the field of computer vision,aiming to address the problem of detecting and locating small objects in images or videos.Traditional object detection algorithms face challenges in handling small objects due to their low resolution,blurriness,and occlusion.To address this issue,a novel approach was proposed for small foreign object detec-tion that combined few-shot learning with attention-based end-to-end networks.In this method,the traditional end-to-end detection network was optimized by introducing image enhancement and attention mechanisms to improve the detection performance.The original data were augmented using data augmentation techniques to increase the diversity and quantity of the data.An attention mechanism was incorporated to extract crucial information from the images and improve the accuracy of the detection results.In terms of network struc-ture,the original feature pyramid network(FPN)was replaced with bi-directional feature pyramid network(BiFPN)to obtain richer image features.Experimental results demonstrate that when the training scale is 640 pixels×640 pixels,the proposed method achieves precision,recall,mean average precision(mAP),and detection speed of 98.41%,99.54%,99.50%,and 28FPS,respectively,surpass-ing mainstream algorithms by improvements of 2.91%,5.9%,1.93%and 2FPS through image augmentation and attention mechanisms.The effectiveness and feasibility of the proposed method are validated.
online detection of small foreign objectsdeep learningimage enhancement technologyattention mechanism
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