YOLOv5_mamba: unmanned aerial vehicle object detection based on bidirectional dense feedback network and adaptive gate feature fusion.

Addressing the problem that the object size in Unmanned Aerial Vehicles (UAVs) aerial images is too small and contains limited feature information, leading to existing detection algorithms having less than ideal performance in small object detection, we propose a UAV aerial object detection system named YOLv5_mamba based on bidirectional dense feedback network and adaptive gate feature fusion. This paper improves the You Only Look Once Version 5 (YOLOv5) algorithm by firstly introducing the Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) module from YOLOv8 into the backbone network to enhance the feature extraction capability of the backbone network. Furthermore, the mamba module and C2f module are introduced to construct a bidirectional dense feedback network to enhance the transfer of contextual information in the neck part.

Accurate UAV Small Object Detection Based on HRFPN and EfficentVMamba.

(1) Background: Small objects in Unmanned Aerial Vehicle (UAV) images are often scattered throughout various regions of the image, such as the corners, and may be blocked by larger objects, as well as susceptible to image noise. Moreover, due to their small size, these objects occupy a limited area in the image, resulting in a scarcity of effective features for detection. (2) Methods: To address the detection of small objects in UAV imagery, we introduce a novel algorithm called High-Resolution Feature Pyramid Network Mamba-Based YOLO (HRMamba-YOLO).

3D residual attention hierarchical fusion for real-time detection of the prostate capsule.

Background: For prostate electrosurgery, where real-time surveillance screens are relied upon for operations, manual identification of the prostate capsule remains the primary method. With the need for rapid and accurate detection becoming increasingly urgent, we set out to develop a deep learning approach for detecting the prostate capsule using endoscopic optical images.

Methods: Our method involves utilizing the Simple, Parameter-Free Attention Module(SimAM) residual attention fusion module to enhance the extraction of texture and detail information, enabling better feature extraction capabilities.

Using SimAM and SSD to Detect Prostate Capsule.

Aiming at the problem of accurate detection of the prostate capsule, this paper designed an accurate detection network of the prostate capsule by integrating 3d non-parametric residual attention mechanism SimAM and SSD, whcih called Attention based on Single Shot Multibox Detector (ASSD). Compared with other mentioned methods, ASSD got the best detetion precision.

Salt-and-pepper Noise Reduction for Medical Images Based on Image Fusion.

Background: During the collection process, the prostate capsule is prone to introduce salt and pepper noise due to gastrointestinal peristalsis, which will affect the precision of subsequent object detection.

Objective: A cascade optimization scheme for image denoising based on image fusion was proposed to improve the peak signal-to-noise ratio(PSNR) and contour protection performance of heterogeneous medical images after image denoising.

Method: Anisotropic diffusion fusion (ADF) was used to decompose the images denoised by adaptive median filter, non-local adaptive median filter and artificial neural network to generate the base layer and detail layer, which were fused by weighted average and Karhunen-Loeve Transform respectively.

Corrigendum to: Application of Two New Feature Fusion Networks to Improve Realtime Prostate Capsula Detection.

We apologize for the error that occurred in the online version of the article. Table 3 was missing in the article entitled "Application of Two New Feature Fusion Networks to Improve Real-time Prostate Capsula Detection" in "Current Medical Imaging", 2021; 17(9): 1128-36 [1]. The original article can be found online at https://www.

Application of Two New Feature Fusion Networks to Improve Real-time Prostate Capsula Detection.

Background: Excess prostate tissue is trimmed near the prostate capsula boundary during transurethral plasma kinetic enucleation of prostate (PKEP) and transurethral bipolar plasmakinetic resection of prostate (PKRP) surgeries. If a large portion of the tissue is removed, a prostate capsula perforation can potentially occur. As such, real-time accurate prostate capsula (PC) detection is critical for the prevention of these perforations.

Vertical transmission of Chlamydia trachomatis in Chongqing China.

This is the first study to investigate vertical transmission of Chlamydia trachomatis in Chongqing China. For this study, 300 cervical swab samples from pregnant women and 305 nasopharygeal swab samples from their babies (605 specimens) were collected for nest polymerase chain reaction (nPCR) of the ompl gene, which encodes the major outer membrane protein (MOMP) and typed C. trachomatis using Cleavase fragment-length polymorphism (CFLP) labeled with digoxin.

[Development of a gap ligase chain reaction for detection of Chlamydia trachomatis in newborn infants].

Objective: To establish a gap ligase chain reaction (G-LCR) assay for the detection of Chlamydia trachomatis (Ct) in neonates with pneumonia.

Methods: A G-LCR DNA amplification assay that targeted the outer major membrane protein gene (omp1) of Ct was developed to detect Ct. The sensitivity and specificity of the G-LCR test was examined by the use of highly purified elementary bodies (EBs).