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Early studies about territory defense of territorial male butterflies were focused on factors that may affect the outcome of contest. But rapid detection was so critical for territorial defense. The detection ability was correlated to visual system. Studies had focused on visual system such as the structure of eye optics and electrophysiology. However, it still existed one question about how do the owner detect intruder in the field. Some factors may affect the probability of detecting intruders from an owner, such as the distance, the size of the intruder, and background contrast. To determine what factor might affect detection ability of territorial male butterfly Hypolimnas bolina, two different sized butterfly models and four different luminance models were used to determine: 1. The reaction rate of the owner with differrent distances; 2. Test the detection ability at different relative position between intruders and owner (acute zone). 3. To test the visual angle hypothesis; 4. Test the luminance contrast effect. The results showed that the response rate decreased with distance but increased with model size. The owner had greater detection ability when the model was presented in the front rather than it on the side. Finally, the response rate was increased with model¡¦s low luminance. Besides, if the model was darker than its background, the owner¡¦s detection ability was greater. Previous studies indicated that a complex background may let the owner spend more time in detection. However, it was quite different with Hypolimnas bolina. In fact, the owner could quickly detect the model when the model was in a complex background, even there was without luminance contrast between the model and background.

In recent years, the rapid advancement of Unmanned Aircraft Systems (UAS) has led to an increasingly complex National Airspace System (NAS), necessitating a comprehensive understanding of factors that impact pilot visual acquisition and detection of other aircraft (including manned fixed-wing, rotorcraft, and UAS). The objective of this study is to investigate factors that affect pilot performance in visually acquiring and detecting other manned-fixed wing aircraft and manned rotorcraft using a multi-method approach, incorporating qualitative and quantitative data analysis. A diverse sample of pilots with varying flight experience participated in the study. Participants were exposed to a series of flight test scenarios in a high-fidelity flight test campaign using different flight paths and detecting different types of aircraft, designed to replicate real-world airspace encounters with other aircraft. Post-flight interviews were conducted, and situational awareness questionnaires and NASA Task Load Index (NASA-TLX) were administered to capture insights on the pilots’ experiences. The goal was to determine the level at which aircraft characteristics, test subjects’ situational awareness and workload, flight conditions, and environmental conditions influenced visual acquisition and detection. All interviews were subjected to several cycles of meticulous coding and subcoding processes to discern both individual and co-occurring factors affecting visual detection capabilities. Additionally, a rigorous statistical analysis was executed on the data derived from the situational awareness questionnaires and NASA-TLX to extract quantitative insights into pilot-centric metrics influencing visual detection. The amalgamated results from both the qualitative and quantitative analyses were synthesized to construct a comprehensive representation of all variables influencing visual detection, in addition to delineating the parallels between factors that affect visual acquisition in both manned fixed-wing and rotorcraft detection scenarios.

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Railways are a main element of a sustainable transport policy in several countries as they are considered a safe, efficient and green mode of transportation. Owing to these advantages, there is a cumulative request for the railway industry to increase the performance, the capacity and the availability in addition to safely transport goods and people at higher speeds. To meet the demand, large adjustment of the infrastructure and improvement of maintenance process are required.  Inspection activities are essential in establishing the required maintenance, and it is periodically required to reduce unexpected failures and to prevent dangerous consequences.  Maintenance of railway catenary systems is a critical task for warranting the safety of electrical railway operation.Usually, the catenary inspection is performed manually by trained personnel. However, as in all human-based inspections characterized by slowness and lack of objectivity, might have a number of crucial disadvantages and potentially lead to dangerous consequences. With the rapid progress of artificial intelligence, it is appropriate for computer vision detection approaches to replace the traditional manual methods during inspections.  In this thesis, a strategy for monitoring the health of catenary wires is developed, which include the various steps needed to detect anomalies in this component. Moreover, a solution for detecting different types of wires in the railway catenary system was implemented, in which a deep learning framework is developed by combining the Convolutional Neural Network (CNN) and the Region Proposal Network (RPN).