The invention discloses an X-ray image target detection method, a device and equipment. The method is mainly based on a multi-classification multi-label structure, a plurality of labels are given to an undetermined frame by obtaining a plurality of candidate labels of the undetermined frame in an X-ray image, and the category of a target in the undetermined frame is determined based on the plurality of candidate labels, that is, the finally determined category of the target in the undetermined frame is not limited to one or more categories. According to the method, regularization of the targetdetection tool can be enhanced; the target detection tool gain is effectively improved; target recognition rate is improved, through verification, the generalization ability of the target detection tool can be improved during specific implementation; compared with the prior art, prohibited goods can be conveniently, rapidly and accurately classified, safety inspectors are assisted in work, meanwhile, the prohibited goods detection rate is greatly increased, the attention point of the method focuses on classification tasks and is irrelevant to regression tasks, the influence on the whole system is small, independence is improved, implementation of technicians in the field is facilitated, and creation of the method is achieved.

X-ray image target detection method, device and equipment

The invention discloses a face quality evaluation method combined with face detection. The method comprises the steps of inputting a to-be-detected picture and performing feature extraction; secondly,extracting features of different scales to perform multi-scale feature fusion; and finally, performing multi-task training by a network, respectively outputting face confidence, face frame coordinates and face quality, screens out a priori frame representing a face according to the face confidence, and performing regression on the priori frame according to the face frame coordinates to obtain a finally detected face frame, and the face quality classification corresponding to the priori frame is the quality classification of the face frame after regression. According to the method, two tasks of face detection and face quality evaluation are realized in the same network, so that multi-task learning is realized; and the GPU video memory is effectively saved, and meanwhile, the speed is higher.

Face quality evaluation method combined with face detection

The invention relates to the technical field of intelligent traffic control, specifically to an intelligent traffic light control method and a system thereof. The method comprises the following steps:collecting traffic flow images in a traffic flow direction at set time intervals, and recognizing the number of vehicles contained in the traffic flow images; obtaining the color duration of a secondtraffic light, wherein the color duration of the second traffic light comprises the duration that the second traffic light is displayed to be red and the duration that the second traffic light is displayed to be green; and controlling the color of a first traffic light and the color of the second traffic light according to the number of vehicles and the color duration of the second traffic light,wherein the first traffic light is an indicator light for guiding the passage of vehicles, and the second traffic light is an indicator light for guiding the passage of pedestrians. According to theinvention, the traffic lights can be rapidly and effectively adjusted according to the actual congestion condition of the road, so that the purpose of traffic optimization is achieved, and smooth passage of the road is guaranteed.

Intelligent traffic light control method and system

The invention discloses an anchor-frame-free multi-phase liver lesion detection method and system, and the method employs an anchor-frame-free method for the first time for the detection of a multi-phase liver lesion, solves a problem of manual adjustment of parameters, and greatly improves the detection efficiency. According to the full-scale connection cycle deep aggregation detection network provided by the invention, shallow and deep features can be fully combined through the network, and shallow feature information is reserved, so that the learning ability of the network to multi-scale features of a focus is improved; according to the invention, a cyclic feature connection module is provided, and the multi-phase dynamic change mode of the scale can be extracted through the module in combination with the features of each scale of the multi-phase image, thereby further improving the detection capability of the multi-phase image for the lesion.

Anchor-frame-free multi-phase liver lesion detection method and system

The invention discloses a single-step face detection system. The invention provides a real-time face detection network YOMO formed by depth separable convolution, the real-time face detection network YOMO comprises a plurality of feature fusion structures in a top-to-bottom form, and each detection module is only responsible for detecting the faces in a corresponding scale range. According to the invention, a random cutting strategy better conforming to a multi-scale detection structure is adopted, so that each detection module can be trained by a larger number of samples. According to an elliptic regression device provided by the invention, the detection recall rate under the ContROC assessment standard can be greatly improved. According to the detection precision of the YOMO model provided by the invention, the detection rate of the 544 * 544 resolution pictures is 51 FPS while the relatively higher competitiveness is kept, and the memory occupation of the model is only 21 M.

Single-step face detection system

The invention discloses a pedestrian and vehicle detection method and system based on improved YOLOv3. According to the method, an improved YOLOv3 network based on Darknet-33 is adopted as a main network to extract features; the cross-layer fusion and reuse of multi-scale features in the backbone network are carried out by adopting a transmittable feature map scale reduction method; and then a feature pyramid network is constructed by adopting a scale amplification method. In the training stage, a K-means clustering method is used for clustering the training set, and the cross-to-parallel ratio of a prediction frame to a real frame is used as a similarity standard to select a priori frame; and then the BBox regression and the multi-label classification are performed according to the loss function. And in the detection stage, for all the detection frames, a non-maximum suppression method is adopted to remove redundant detection frames according to confidence scores and IOU values, and an optimal target object is predicted. According to the method, a feature extraction network Darknet-33 of feature map scale reduction fusion is adopted, a feature pyramid is constructed through feature map scale amplification migration fusion, and a priori frame is selected through clustering, so that the speed and precision of the pedestrian and vehicle detection can be improved.

A pedestrian and vehicle detection method and system based on improved YOLOv3

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A pedestrian and vehicle detection method and system based on improved YOLOv3