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• This paper establishes an image dataset of sika deer obtained from different perspectives. • This paper proposes an AD-YOLOv5 model to detect key parts of the sika deer. • This paper improves the loss function of the model to enhance both the training and inference ability of the model. • This paper proposes an adaptive channel attention mechanism for better extracting shallow features of the key parts of sika deer. • By altering the feature fusion network and combining it with the channel attention mechanism, the feature information is balanced across different scales, which enhances the accuracy of detection. Accurate and rapid fixation of key parts of sika deer is essential for the mechanized and intelligent collection of both deer blood and velvet antler. To solve the low detection accuracy problem of key parts of deer caused by the diversity of deer farm breeding environment and the rapid movement of deer, this paper proposes an AD-YOLOv5 algorithm for detecting key parts of sika deer based on YOLOv5s. Specifically, a new weighted bidirectional feature pyramid network S-BiFPN is first proposed, which both simplifies the structure and effectively performs skip feature fusion to better capture object information at different scales, so that the adaptive feature fusion capability of the model is optimized. Second, this paper proposes to add a SENet module after the C3 module of the 9th Layer in the backbone part of the network, so that the network can adaptively readjust the channel weights of feature maps to enhance the important features for key parts of sika deer and improve the expression ability of the model for key parts. Furthermore, to make the model more stable in the training process, a novel bounding box regression loss function SIoU is led into, which can better learn the position and size of the bounding box by fusing the orientation information between the ground truth box and the predicted box. The effectiveness of the proposed algorithm is demonstrated through ablation experiments. Experimental results show that the mAP @0.5 of the proposed algorithm reaches 97.30 % for the detection of key parts of sika deer, which is 12.85 %, 9.65 %, 9.18 %, 7.13 %, 3.59 %, 4.6 %, 0.90 % and 0.10 % higher than SSD, EfficientDet, YOLOv3, YOLOv4, Faster R-CNN, YOLOv5s, YOLOv7 and YOLOv8s, respectively. This research provides a new idea for detecting key parts of animals in complex environments and lays a theoretical foundation for the development of intelligent fixation devices for sika deer. [ABSTRACT FROM AUTHOR]

• Constructed a dataset of early growth stages of clam seedlings under the microscope. • Four different attention mechanisms were employed in ablation experiments for discussion. • The K-means++ algorithm was used to enhance anchor accuracy, and Soft-NMS was applied to solve missed detections due to dense stacking of clam seedlings. • Heatmaps were constructed for visualization, analysis the effects of attention mechanism. • Software for automatic detection of clam seedlings biological features was developed. In order to monitor the early growth status of clam seedlings and meet the demands of precision seedling cultivation in a factory setting, this study improves the YOLOv5s model and proposes a high-precision and lightweight method for detecting biological features of clam seedlings. Firstly, dataset of clam seedlings under the microscope was constructed. Secondly, we established a clam seedling detection model based on four different attention mechanisms and analyzed the differences in the features focused on by different attention mechanisms in clam seedling detection. The accuracy of the anchors was optimized using the K-means++ algorithm, and the Soft-NMS method was employed to address the issue of missed detections caused by dense stacking of clam seedlings. The proposed approach has the optimal comprehensive performance by comparing various lightweight networks. The results show that the AP-health, AP-death and mAP of this approach are 98.15%, 93.87% and 96.01%, which can meet the standard for high precision. With a transmission speed of 86 FPS, an average response time of 11.54 ms, and a model size of 27.59 MB, the approach satisfies the requirements for efficiency and portability. Finally, a biological feature detection software of clam seedlings was developed, which can automatically calculate phenotypic information such as the number of clam seedlings, survival rate, and average size. This paper provides a basis for real-time and accurate assessment of clam seedlings biological features while offering technical support for the automation and intelligence of clam seedlings production in factories. [ABSTRACT FROM AUTHOR]