Abstract: Soil-dwelling termites are highly destructive pests that pose significant threats to hydraulic engineering structures and garden trees. These pests are notorious for causing extensive damage, with their activity often characterized by mud covering, mud tunnel, and swarming hole. Early and accurate identification of these activity signs is essential for implementing timely and effective termite damage early warning systems and control measures. Such measures are critical for minimizing economic losses, preserving ecological integrity, and ensuring the safety of infrastructure. However, detecting the activity signs of soil-dwelling termites is a challenging task, primarily due to the complexity of natural environments, diverse background interference, and the frequent difficulty in distinguishing termite activity signs from their surrounding environment, particularly in cases where the colors of the signs and the background blend together.To address these practical challenges, this study proposed an advanced one-stage object detection algorithm named ACP-YOLOv5s. This algorithm is an improved version of the widely used YOLOv5s model and has been specifically optimized to enhance feature extraction and color perception capabilities in complex natural environments. The core innovation lies in the integration of an adaptive color perception module (ACP-Module) along with a dynamically adjustable threshold learning mechanism. The ACP-Module intelligently analyzes the color distribution of input images and dynamically determines the optimal color threshold range. This allows the model to automatically adjust its sensitivity to colors based on varying image contents. This mechanism effectively mitigates detection instability caused by color confusion, significantly enhancing the stability and generalization capabilities of the model. As a result, the model performs exceptionally well even in complex scenarios with high levels of environmental noise and interference.To further enhance detection accuracy, a CARFE upsampling module was incorporated into the neck structure of the model. This module plays a pivotal role in expanding the receptive field and reorganizing feature information, enabling the model to detect finer image details with greater precision. The design of the CARFE module greatly enhances feature fusion and transmission, which is critical for improving detection accuracy in real-world applications. This optimization not only boosts the model’s performance across various environmental conditions but also significantly improves its ability to detect subtle termite activity signs, such as mud covering, mud tunnel, and swarming hole.Extensive experimental validation of the ACP-YOLOv5s model demonstrated its superiority in detecting soil-dwelling termite activity signs. The model achieved a remarkable precision of 91.2%, outperforming several state-of-the-art models, including Faster R-CNN, YOLOv5s, YOLOv5m, YOLOv8, and YOLOv9, by 5.3, 5.0, 3.4, 7.9, and 0.1 percentage points, respectively. Furthermore, the model attained a mean average precision (mAP50) of 92.3%, representing improvements of 6.7, 2.9, 1.4, 2.2, and 0.4 percentage points over these models. These results underscore the effectiveness of ACP-YOLOv5s in enhancing detection accuracy and adaptability to complex environmental conditions. The model provides a robust and reliable technical solution for early warning and precise control of termite infestations.The integration of the ACP-Module and CARFE upsampling module sets the ACP-YOLOv5s model apart from its predecessors by addressing key challenges in detecting termite activity signs under natural and often unpredictable conditions. Its ability to accurately identify termite activity in diverse environments ensures its applicability to a wide range of scenarios, from forested areas to urban landscapes. The technological advancements embedded in the ACP-YOLOv5s model not only optimize existing object detection methodologies but also provide critical improvements in precision, stability, and generalization. The ACP-YOLOv5s model represents a significant step forward in object detection technologies for pest management. Its development offers a powerful tool for identifying and mitigating soil-dwelling termite activity, providing substantial benefits for hydraulic engineering and garden tree maintenance. By ensuring the timely detection and precise control of termite infestations, this model makes meaningful contributions to infrastructure safety, ecological preservation, and the advancement of pest control technologies.