Single Image Detection Tests

Task: AZ-140_test_single_image Name: Single Image Detection Tests Description: Implement E2E tests verifying single image detection, confidence filtering, overlap deduplication, physical size filtering, and weather mode classes Complexity: 3 points Dependencies: AZ-138_test_infrastructure Component: Integration Tests Jira: AZ-140 Epic: AZ-137

Problem

Single image detection is the core functionality of the system. Tests must verify that detections are returned with correct structure, confidence filtering works at different thresholds, overlapping detections are deduplicated, physical size filtering removes implausible detections, and weather mode class variants are recognized.

Outcome

Detection response structure validated (x, y, width, height, label, confidence)
Confidence threshold filtering confirmed at multiple thresholds
Overlap deduplication verified with configurable containment ratio
Physical size filtering validated against MaxSizeM from classes.json
Weather mode class variants (Norm, Wint, Night) recognized correctly

Scope

Included

FT-P-03: Single image detection returns detections
FT-P-05: Detection confidence filtering respects threshold
FT-P-06: Overlapping detections are deduplicated
FT-P-07: Physical size filtering removes oversized detections
FT-P-13: Weather mode class variants

Excluded

Large image tiling (covered in tiling tests)
Async/video detection (covered in async and video tests)
Negative input validation (covered in negative tests)

Acceptance Criteria

AC-1: Detection response structure Given an initialized engine and a valid small image When POST /detect is called with the image Then response is 200 with an array of DetectionDto objects containing x, y, width, height, label, confidence fields with coordinates in 0.0-1.0 range

AC-2: Confidence filtering Given an initialized engine When POST /detect is called with probability_threshold 0.8 Then all returned detections have confidence >= 0.8 And calling with threshold 0.1 returns >= the number from threshold 0.8

AC-3: Overlap deduplication Given an initialized engine and a scene with clustered objects When POST /detect is called with tracking_intersection_threshold 0.6 Then no two detections of the same class overlap by more than 60% And lower threshold (0.01) produces fewer or equal detections

AC-4: Physical size filtering Given an initialized engine and known GSD parameters When POST /detect is called with altitude, focal_length, sensor_width config Then no detection's computed physical size exceeds the MaxSizeM for its class

AC-5: Weather mode classes Given an initialized engine with classes.json including weather variants When POST /detect is called Then all returned labels are valid entries from the 19-class x 3-mode registry

Non-Functional Requirements

Performance

Single image detection within 30s (includes potential engine init)

Integration Tests

AC Ref	Initial Data/Conditions	What to Test	Expected Behavior	NFR References
AC-1	Engine warm, small-image	POST /detect response structure	Array of DetectionDto, coords 0.0-1.0	Max 30s
AC-2	Engine warm, small-image	Two thresholds (0.8 vs 0.1)	Higher threshold = fewer detections	Max 30s
AC-3	Engine warm, small-image	Two containment thresholds	Lower threshold = more dedup	Max 30s
AC-4	Engine warm, small-image, GSD config	Physical size vs MaxSizeM	No oversized detections returned	Max 30s
AC-5	Engine warm, small-image	Detection label validation	Labels match classes.json entries	Max 30s

Constraints

Deduplication verification requires the test image to produce overlapping detections
Physical size filtering requires correct GSD parameters matching the fixture image
Weather mode verification depends on classes.json fixture content

Risks & Mitigation

Risk 1: Insufficient detections from test image

Risk: Small test image may not produce enough detections for meaningful filtering/dedup tests
Mitigation: Use an image with known dense object content; accept >= 1 detection as valid

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