Printed circuit boards (PCBs) are extensively used to assemble electronic equipment. Currently, PCBs are an integral part of almost all electronic products. However, various surface defects can still occur during mass production. An enhanced YOLOv5s network named YOLO-MBBi is proposed to detect surface defects on PCBs to address the shortcomings of the existing PCB surface defect detection methods, such as their low accuracy and poor real-time performance. YOLO-MBBi uses MBConv (mobile inverted residual bottleneck block) modules, CBAM attention, BiFPN, and depth-wise convolutions to substitute layers in the YOLOv5s network and replace the CIoU loss function with the SIoU loss function during training. Two publicly available datasets were selected for this experiment. The experimental results showed that the mAP50 and recall values of YOLO-MBBi were 95.3% and 94.6%, which were 3.6% and 2.6% higher than those of YOLOv5s, respectively, and the FLOPs were 12.8, which was much smaller than YOLOv7’s 103.2. The FPS value reached 48.9. Additionally, after using another dataset, the YOLO-MBBi metrics also achieved satisfactory accuracy and met the needs of industrial production.

Given the limited computing capabilities of UAV terminal equipment, there is a challenge in balancing the accuracy and computational cost when deploying the target detection model for forest fire detection on the UAV. Additionally, the fire targets photographed by the UAV are small and prone to misdetection and omission during detection. This paper proposes a lightweight, small target detection model, FL-YOLOv7, based on YOLOv7. First, we designed a light module, C3GhostV2, to replace the feature extraction module in YOLOv7. Simultaneously, we used the Ghost module to replace some of the standard convolution layers in the backbone network, accelerating inference speed and reducing model parameters. Secondly, we introduced the Parameter-Free Attention (SimAm) attention mechanism to highlight the features of smoke and fire targets and suppress background interference, improving the model’s representation and generalization performance without increasing network parameters. Finally, we incorporated the Adaptive Spatial Feature Fusion (ASFF) module to address the model’s weak small target detection capability and use the loss function with dynamically adjustable sample weights (WIoU) to weaken the impact of low-quality or complex samples and improve the model’s overall performance. Experimental results show that FL-YOLOv7 reduces the parameter count by 27% compared to the YOLOv7 model while improving 2.9% ${mAP50}_{small}$ and 24.4 frames per second in FPS, demonstrating the effectiveness and superiority of our model in small target detection, as well as its real-time and reliability in forest fire scenarios.

Façade defects not only detract from the building’s aesthetics but also compromise its performance. Furthermore, they potentially endanger pedestrians, occupants, and property. Existing deep-learning-based methodologies are facing some challenges in terms of recognition speed and model complexity. An improved YOLOv7 method, named BFD-YOLO, is proposed to ensure the accuracy and speed of building façade defects detection in this paper. Firstly, the original ELAN module in YOLOv7 was substituted with a lightweight MobileOne module to diminish the quantity of parameters and enhance the speed of inference. Secondly, the coordinate attention module was added to the model to enhance feature extraction capability. Next, the SCYLLA-IoU was used to expedite the rate of convergence and increase the recall of the model. Finally, we have extended the open datasets to construct a building façade damage dataset that includes three typical defects. BFD-YOLO demonstrates excellent accuracy and efficiency based on this dataset. Compared to YOLOv7, BFD-YOLO’s precision and mAP@.5 are improved by 2.2% and 2.9%, respectively, while maintaining comparable efficiency. The experimental results indicate that the proposed method obtained higher detection accuracy with guaranteed real-time performance.

In the field of metallurgy, the timely and accurate detection of surface defects on metallic materials is a crucial quality control task. However, current defect detection approaches face challenges with large model parameters and low detection rates. To address these issues, this paper proposes a lightweight recognition model for surface damage on steel strips, named LSD-YOLOv5. First, we design a shallow feature enhancement module to replace the first Conv structure in the backbone network. Second, the Coordinate Attention mechanism is introduced into the MobileNetV2 bottleneck structure to maintain the lightweight nature of the model. Then, we propose a smaller bidirectional feature pyramid network (BiFPN-S) and combine it with Concat operation for efficient bidirectional cross-scale connectivity and weighted feature fusion. Finally, the Soft-DIoU-NMS algorithm is employed to enhance the recognition efficiency in scenarios where targets overlap. Compared with the original YOLOv5s, the LSD-YOLOv5 model achieves a reduction of 61.5% in model parameters and a 28.7% improvement in detection speed, while improving recognition accuracy by 2.4%. This demonstrates that the model achieves an optimal balance between detection accuracy and speed, while maintaining a lightweight structure.

Electronic components are the main components of PCBs (printed circuit boards), so the detection and classification of ECs (electronic components) is an important aspect of recycling used PCBs. However, due to the variety and quantity of ECs, traditional target detection methods for EC classification still have problems such as slow detection speed and low performance, and the accuracy of the detection needs to be improved. To overcome these limitations, this study proposes an enhanced YOLO (you only look once) network (EC-YOLOv7) for detecting EC targets. The network uses ACmix (a mixed model that enjoys the benefits of both self-attention and convolution) as a substitute for the 3 × 3 convolutional modules in the E-ELAN (Extended ELAN) architecture and implements branch links and 1 × 1 convolutional arrays between the ACmix modules to improve the speed of feature retrieval and network inference. Furthermore, the ResNet-ACmix module is engineered to prevent the leakage of function data and to minimise calculation time. Subsequently, the SPPCSPS (spatial pyramid pooling connected spatial pyramid convolution) block has been improved by replacing the serial channels with concurrent channels, which improves the fusion speed of the image features. To effectively capture spatial information and improve detection accuracy, the DyHead (the dynamic head) is utilised to enhance the model’s size, mission, and sense of space, which effectively captures spatial information and improves the detection accuracy. A new bounding-box loss regression method, the WIoU-Soft-NMS method, is finally suggested to facilitate prediction regression and improve the localisation accuracy. The experimental results demonstrate that the enhanced YOLOv7 net surpasses the initial YOLOv7 model and other common EC detection methods. The proposed EC-YOLOv7 network reaches a mean accuracy (mAP@0.5) of 94.4% on the PCB dataset and exhibits higher FPS compared to the original YOLOv7 model. In conclusion, it can significantly enhance high-density EC target recognition.

Currently, X-ray inspection systems may produce false detections due to factors such as the varying sizes of contraband images, complex backgrounds, and blurred edges. To address this issue, we propose the YOLO-CID method for contraband image detection. Firstly, we designed the MP-OD module in the backbone network to enhance the model’s ability to extract key information from complex background images. Secondly, at the neck of the network, we designed a simplified version of BiFPN to add cross-scale connection lines in the feature fusion structure, to preserve deeper semantic information and enhance the network’s ability to represent objects in low-contrast or occlusion situations. Finally, we added a new object detection layer to improve the model’s accuracy in detecting small objects in dense environments. Experimental results on the PIDray public dataset show that the average accuracy rate of the YOLO-CID algorithm is 82.7% and the recall rate is 81.2%, which are 4.9% and 3.2% higher than the YOLOv7 algorithm, respectively. At the same time, the mAP on the CLCXray dataset reached 80.2%. Additionally, it can achieve a real-time detection speed of 40 frames per second and 43 frames per second in real scenes. These results demonstrate the effectiveness of the YOLO-CID algorithm in X-ray contraband detection.