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Background Skull fracture, as a common traumatic brain injury, can lead to multiple complications including bleeding, leaking of cerebrospinal fluid, infection, and seizures. Automatic skull fracture detection (SFD) is of great importance, especially in emergency medicine. Purpose Existing algorithms for SFD, developed based on hand‐crafted features, suffer from low detection accuracy due to poor generalizability to unseen samples. Deploying deep detectors designed for natural images like Faster Region‐based Convolutional Neural Network (R‐CNN) for SFD can be helpful but are of high redundancy and with nonnegligible false detections due to the cranial suture and skull base interference. Therefore, we, for the first time, propose an anchor‐efficient anti‐interference deep learning framework named Fracture R‐CNN for accurate SFD with low computational cost. Methods The proposed Fracture R‐CNN is developed by incorporating the prior knowledge utilized in clinical diagnosis into the original Faster R‐CNN. Specifically, based on the distributions of skull fractures, we first propose an adaptive anchoring region proposal network (AA‐RPN) to generate proposals for diverse‐scale fractures with low computational complexity. Then, based on the prior knowledge that cranial sutures exist in the junctions of bones and usually contain sclerotic margins, we design an anti‐interference head (A‐Head) network to eliminate the cranial suture interference for better SFD detection. In addition, to further enhance the anti‐interference ability of the proposed A‐Head, a difficulty‐balanced weighted loss function is proposed to emphasize more on distinguishing the interference areas from the skull base and the cranial sutures during training. Results Experimental results demonstrate that the proposed Fracture R‐CNN outperforms the current state‐of‐the‐art (SOTA) deep detectors for SFD with a higher recall and fewer false detections. Compared to Faster R‐CNN, the proposed Fracture R‐CNN improves the average precision (AP) by 11.74% and the free‐response receiver operating characteristic (FROC) score by 11.08%. Through validating on various backbones, we further demonstrate the architecture independence of Fracture R‐CNN, making it extendable to other detection applications. Conclusions As the customized deep learning–based framework for SFD, Fracture R‐CNN can effectively overcome the unique challenges in SFD with less computational cost, leading to a better detection performance compared to the SOTA deep detectors. Moreover, we believe the prior knowledge explored for Fracture R‐CNN would shed new light on future deep learning approaches for SFD.

An important step in video tracking and identification is moving target detection. The new method combined Gaussian background models with HSV remove space shadow, better adapted to the situation of background changed and updated, avoid segmentation errors caused by shadow. First, the Gaussian model was constructed, and a new way was adopted to update background. Then, a coarse foreground image could be obtained by background subtraction. Next, moving shadows was eliminated by a shadow detection algorithm using hue, saturation and brightness information. Finally, we performed post processing, including morphology filtering, connected component analysis and seeded region growing. The experiments prove using the detection results and mean shift algorithm can get a better tracking.