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Computer vision methods for non-contact detection of herd pigs could help detect early disease and reduce mortality rates by analyzing pig behavior. Due to the limitation of breeding space and cost, the unit breeding area is relatively dense, making it difficult to detect all pigs for a long time without interruption. In order to improve the detection performance, this paper proposes an end-to-end efficient and accurate herd pig detection framework based on YOLOv7 target detection model, which is named Ghost-YOLOv7-SIoU. In this framework, the feature extraction backbone network consists of a series of directly connected efficient layer aggregation networks (ELAN) and downsampling modules. The neck network contains a feature pyramid network and path aggregation network. Ghost convolution is adopted to replace the 3 × 3 standard convolution of the ELAN module in backbone network and the scaled-up ELAN module in neck network to obtain rich features while reducing the parameter number and computational effort. Furthermore, to speed up the model convergence and improve the model robustness and accuracy, SIoU loss is used for bounding box regression in the training stage. On the VOC2012 dataset, the number of parameters and floating-point operations decreased by 13.4% and 15.7% compared to YOLOv7, with comparable detection accuracy. Additionally, the number of parameters and floating-point operations decreased by 13.7% and 16.1% on our pig dataset. Ghost-YOLOv7-SIoU is superior to YOLOV4-CSP and YOLOR-CSP in accuracy. Experimental results demonstrate the effectiveness of the proposed method in improving the efficiency of model detection while ensuring detection accuracy. [ABSTRACT FROM AUTHOR]

Based on the training effect evaluation of swimmers, this paper proposes an infrared image detection method based on HOG feature extraction and infrared image detection technology. Since optical images are easily affected by illumination and other factors in complex environments, infrared images are chosen as the object of training effect evaluation in this study. Infrared images have the advantages of illumination insensitivity, through fog and so on, which can better reflect the posture and action of swimmers. The infrared image detection method based on HOG feature extraction is described in detail, and the feature vector of swimmers is obtained. By detecting the infrared images of the training set and the test set, the evaluation results of the training effect of the swimmers are obtained. The experimental results show that this method can accurately detect the swimmer's posture and movement, and provides an effective means for evaluating the training effect. [ABSTRACT FROM AUTHOR]

In today's increasingly complex traffic environment, pedestrian detection has become increasingly important. The Histogram of Oriented Gradients (HOG) algorithm has been proven to be highly efficient in pedestrian detection. This paper proposes a low-resource consumption, high-speed hardware implementation for HOG algorithm. In the case of a slight sacrifice in accuracy, it increases computational speed and reduces resource consumption. Experimental results demonstrate that the implementation achieves a speed of 0.933 pixels per clock cycle and consumes 4117 look-up tables and 4.5 Kbits of block RAMs while its accuracy decreases by 1.2% on the INRIA dataset and by 0.11% on the MIT dataset. • The design determines the bin number by comparison to avoid complex calculations. • The design calculates the magnitude by approximation to avoid complex calculations. • This design improves computational speed and reduces resource consumption. [ABSTRACT FROM AUTHOR]