Objective: In the task of camouflage target detection, there is a problem that the target is highly integrated with the complex environment background, which is difficult to identify and leads to false detection and missed detection. A target detection algorithm CM-YOLOv5s is proposed for camouflage characteristics. Method: The algorithm uses YOLOv5s as the basic framework. First, a coordinated attention mechanism is embedded in the backbone feature extraction network, which enhances the network’s ability to extract camouflaged target features, weakens the attention to the surrounding background, and effectively improves the algorithm’s anti-background. Interference ability; secondly, the Mixup data enhancement strategy is used to simulate overlapping occlusion scenarios, which further strengthens the network model’s learning ability for complex samples. Results: The training and verification were carried out on the self-made Military Camouflage Target Dataset (MCTD), and the precision, recall, and average mAP of the improved CM-YOLOv5s algorithm reached 95.9%, 87.1%, and 94.1, respectively. %, compared with the original YOLOv5s model, the average accuracy rate is improved by 3.8 percentage points. Conclusion: The improved algorithm has better detection effect, and realizes accurate identification and rapid positioning of military camouflage targets in complex environments.
In the task of camouflaged human target detection, the target is highly integrated with the complex environment background, which is difficult to identify and leads to false detection and missed detection. A detection algorithm MC-YOLOv5s is proposed for the characteristics of camouflaged targets. The algorithm takes YOLOv5s as the basic framework. First, a multispectral channel attention module is embedded in the backbone feature extraction network, which enhances the network’s ability to extract camouflaged target features, weakens the attention to the surrounding background, and effectively improves the algorithm’s antibackground interference. Second, the original upsampling operation is replaced by a lightweight general upsampling operator to achieve effective fusion of high-resolution low-level feature maps and low-resolution high-level feature maps. Finally, the K-means++ clustering method is used to optimize the anchor boxes of the dataset target, and the sizes of the generated priori boxes are allocated to each detection layer, which increases the matching degree between the priori boxes and the actual target boxes, and further improves the detection accuracy of the algorithm. The training and verification were carried out on the military camouflaged personnel dataset (MCPD), the precision (P), recall (R), and mean average precision (mAP) of the MC-YOLOv5s algorithm reached 97.4%, 86.1%, and 94%, respectively. Compared to the original YOLOv5s model, mean average precision (mAP) is increased by 3.7 percentage points. The improved algorithm has better detection effect, is more sensitive to camouflaged targets, and achieves accurate positioning and identification of camouflaged human targets. If the proposed MC-YOLOv5s is applied to personnel search and rescue in complex battlefields and natural disaster environments, it can greatly improve personnel search and rescue efficiency and life survival rate, and reduce the consumption of human and material resources in rescue.
Aiming at the problem of error and omission caused by the complex image background and the high similarity between the remote sensing objectt and the background in remote sensing image detection, an improved model of remote sensing image detection based on YOLOv5s is proposed. The dual attention mechanism of channel and space is added to the convolutional layer of the trunk feature extraction network and the feature fusion network, which enhances the features with high correlation of the objectt to be detected and suppresses the features with low correlation, and improves the ability of the model to extract the features of remote sensing objectts in a complex background, so as to improve the detection accuracy of the algorithm. In this study, a comparative experiment was conducted on the NWPU VHR-10 dataset, and the results showed that the average accuracy of the proposed model was 94% when the dataset was turned over and the ratio was 0.5, which was 2.3% higher than that of the original YOLov5s, which verified the effectiveness of the proposed method.
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