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.
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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