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Object detection algorithms play a crucial role in other vision tasks. This paper finds that the action recognition algorithm SlowFast's detection algorithm FasterRCNN (Region Convolutional Neural Network) has disadvantages in terms of both detection accuracy and speed and the traditional IOU (Intersection over Union) localization loss is difficult to make the detection model converge to the minimum stability point. To solve the above problems, the article uses YOLOv3 (You Only Look Once), YOLOX, and CascadeRCNN to improve the detection accuracy and speed of the SlowFast. This paper proposes a new localization loss function that adopts the Lance and Williams distance as a new penalty term. The new loss function is more sensitive when the distance difference is smaller, and this property is very suitable for the late convergence of the detection model. The experiments were conducted on the VOC (Visual Object Classes) dataset and the COCO dataset. In the final videos test, YOLOv3 improved the detection speed by 10.5 s. CascadeRCNN improved by 3.1%AP compared to FasterRCNN in the COCO dataset. YOLOX's performance on the COCO dataset is also mostly better than that of FasterRCNN. The new LIOU (Lance and Williams Distance Intersection over Union) localization loss function performs better than other loss functions in the VOC dataset. It can be seen that improving the detection algorithm of the SlowFast seems to be crucial and the proposed loss function is indeed effective. [ABSTRACT FROM AUTHOR]

Feature extraction networks play a crucial role in classification algorithms. However, most convolutional neural networks use the form of residual block stacking to extract downstream features of images, and this simple form is not sufficient to obtain better classification performance. Therefore, this paper proposes a YOLOX detection framework based on an improved ConvNeXt which incorporates many advantages of Swin-Transformer with efficient classification performance. To address the problems of unstable IOU convergence and inaccurate regression coordinates of the algorithm, this paper adopts Manhattan distance as the new penalty term, and the new loss function combines the advantages of Euclidean distance and Manhattan distance, which has no gradient explosion in the early training period and can drop smoothly to a stable point in the late training period. The proposed algorithm is robust to negative effects such as dust, light, shadows, and occlusions, and is well-suited for masked-face detection in industrial and public scenarios. The proposed model is tested on the VOC dataset and the AIZOO open-source masked-face dataset, obtaining 5.58% AP improvement on the VOC dataset and reaching 96.665% AP on the masked-face dataset, which is better than other algorithms. • Designing a deep neural network to use for masked-face detection. • Anchor-free model is used to avoid time-consuming post-processing. • The improved ConvNeXt network improves classification performance. • Adopting Manhattan distance-based localization loss makes training stable. • The Mosaic data augmentation strategy alleviates the overfitting problem. [ABSTRACT FROM AUTHOR]