Mr. Hongcheng Xue Profile

Hongcheng Xue

at Inner Mongolia Univ of Technology

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Proceedings Article | 22 October 2021 Paper

Small target modified car parts detection based on improved Faster-RCNN

Hongcheng Xue, Junping Qin, Wei Ren, Chao Quan, Tong Gao

Proceedings Volume 11928, 1192810 (2021) https://doi.org/10.1117/12.2611408

KEYWORDS: Target detection, Detection and tracking algorithms, Performance modeling, Roads, Bismuth, Data modeling, Target recognition, Statistical modeling, Signal attenuation, Safety

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Because the small target image has fewer pixels, it is prone to miss detection and error detection; the target detection algorithm needs to be improved and enhanced to deal with the problem about the small target detection in specific scenarios. In this paper, small target detection algorithm is applied to the new field of illegally modified vehicle detection to reduce the workload of traffic management. The commonest modification is to install a rear wing and change the contact angle between the hub and the ground. This paper proposes a method to detect modified car parts based on improving Faster-Rcnn, in order to detect two modifications described above. On the basis of the original Faster-Rcnn, using multi-scale training and increasing the number of Anchors to enhance the robustness of the network in detecting targets of different sizes, and introducing the Soft-NMS algorithm to replace the NMS algorithm, to solve the problem of partial overlap between two targets when the distance is close, the possibility of missed detection of targets with low confidence and bounding boxes with high confidence scores are not always more reliable than bounding boxes with low confidence. Experiments show that compared with the original Faster-Rcnn, the detection accuracy is increased by 4.6%, and the model has a certain generalization ability and robustness.

Proceedings Article | 27 November 2019 Paper

Sheep delivery scene detection based on faster-RCNN

Shiwen Sun, Junping Qin, Hongcheng Xue

Proceedings Volume 11321, 113211C (2019) https://doi.org/10.1117/12.2538904

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Sheep delivery scene detection is one of the important applications of object detection technology in the field of animal delivery detection. At present, there are reports on the detection of delivery scenarios of pigs and dairy cows at home and abroad, but the research on the behavior of sheep delivery is still in its infancy. This paper aims to apply the Faster- RCNN model for the detection of ewes and newborn lambs in a sheep delivery scenario; Training the Faster-RCNN model based on the ZF, VGG16 feature extraction networks and the Soft-NMS algorithm respectively by using the selfmade sheep delivery scene data set, and the experimental results were compared; The comparison of experimental results show that the Faster-RCNN model based on Soft-NMS algorithm and VGG16 feature extraction network has better effect in sheep delivery scene detection. The method can effectively complete the detection of the ewes and the newborn lambs in the sheep delivery scene, expand the application range of artificial intelligence in the animal husbandry, and has certain popularization and application value for promoting the development of the wisdom animal husbandry.

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